SQL_Hypothesis_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mod 3 Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student Info\n",
    "\n",
    "- Name: Andi Osika\n",
    "- Cohort: ptds_100719\n",
    "- Instructor: James Irving, PhD\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access to a pseudo company database called Northwind Traders was provided to develop and test four hypotheses to collect business insight and deliver appropriate recommendations.  The SQL database schema is provided below that outlines various tables which contain product, sales order, employee and customer information.  Upon reviewing the tables and data contained within them, it was determined to focus of this series of tests would be around maximizing revenues.\n",
    "\n",
    "The questions to be answered are listed below and the links lead to the series corresponding hypotheses testing and, visualizations that support the answer each question which is deliverd in the \"Findings\" subheading. As expected, any recommendations can be found in the \"Recommendation\" subheading of each section.\n",
    "\n",
    "[Question 1 / Hypothesis 1](#HYPOTHESIS-1)\n",
    "> **Does discount amount have a statistically significant effect on the quantity of a product in an order? If so, at what level(s) of discount?** \n",
    "\n",
    "[Question 2 / Hypothesis 2](#HYPOTHESIS-2)\n",
    "> **Do some categories statistically generate more revenue than others ?? Which ones?**\n",
    "\n",
    "[Question 3 / Hypothesis 3](#HYPOTHESIS-3)\n",
    "> ***Do certain sales representatives statistically sell more than others?  Who are the top sellers?***\n",
    "\n",
    "[Question 4/ Hypotheisis 4](#HYPOTHESIS-4)\n",
    "> ***Where are the customers from that spend the most money?  Do some regions generate statistically significantly more revenue than others?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/jirvingphd/dsc-mod-3-project-online-ds-ft-100719/master/Northwind_ERD_updated.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-31T00:26:56.223159Z",
     "start_time": "2020-01-31T00:26:56.220980Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install -U fsds_100719\n",
    "# from fsds_100719.imports import *\n",
    "\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-31T00:26:48.243683Z",
     "start_time": "2020-01-31T00:26:48.241435Z"
    }
   },
   "outputs": [],
   "source": [
    "import functions as fn\n",
    "\n",
    "## Uncomment the line below to see the source code for the imported functions\n",
    "# fs.ihelp(Cohen_d,False),fs.ihelp(find_outliers_IQR,False), fs.ihelp(find_outliers_Z,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-31T00:27:39.848908Z",
     "start_time": "2020-01-31T00:27:39.845403Z"
    }
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "connect = sqlite3.connect('Northwind_small.sqlite')\n",
    "cur = connect.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List of Tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-31T00:28:03.375218Z",
     "start_time": "2020-01-31T00:28:03.360094Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Employee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Shipper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Supplier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>OrderDetail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>CustomerCustomerDemo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>CustomerDemographic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Territory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>EmployeeTerritory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Table\n",
       "0               Employee\n",
       "1               Category\n",
       "2               Customer\n",
       "3                Shipper\n",
       "4               Supplier\n",
       "5                  Order\n",
       "6                Product\n",
       "7            OrderDetail\n",
       "8   CustomerCustomerDemo\n",
       "9    CustomerDemographic\n",
       "10                Region\n",
       "11             Territory\n",
       "12     EmployeeTerritory"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT name FROM sqlite_master WHERE type='table';\"\"\")\n",
    "df_tables = pd.DataFrame(cur.fetchall(), columns=['Table'])\n",
    "df_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>EmployeeId</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>RequiredDate</th>\n",
       "      <th>ShippedDate</th>\n",
       "      <th>ShipVia</th>\n",
       "      <th>Freight</th>\n",
       "      <th>ShipName</th>\n",
       "      <th>ShipAddress</th>\n",
       "      <th>ShipCity</th>\n",
       "      <th>ShipRegion</th>\n",
       "      <th>ShipPostalCode</th>\n",
       "      <th>ShipCountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248</td>\n",
       "      <td>VINET</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-07-04</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Vins et alcools Chevalier</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10249</td>\n",
       "      <td>TOMSP</td>\n",
       "      <td>6</td>\n",
       "      <td>2012-07-05</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>1</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Toms Spezialitäten</td>\n",
       "      <td>Luisenstr. 48</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10250</td>\n",
       "      <td>HANAR</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-07-08</td>\n",
       "      <td>2012-08-05</td>\n",
       "      <td>2012-07-12</td>\n",
       "      <td>2</td>\n",
       "      <td>65.83</td>\n",
       "      <td>Hanari Carnes</td>\n",
       "      <td>Rua do Paço, 67</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>South America</td>\n",
       "      <td>05454-876</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10251</td>\n",
       "      <td>VICTE</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-08</td>\n",
       "      <td>2012-08-05</td>\n",
       "      <td>2012-07-15</td>\n",
       "      <td>1</td>\n",
       "      <td>41.34</td>\n",
       "      <td>Victuailles en stock</td>\n",
       "      <td>2, rue du Commerce</td>\n",
       "      <td>Lyon</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>69004</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10252</td>\n",
       "      <td>SUPRD</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-07-09</td>\n",
       "      <td>2012-08-06</td>\n",
       "      <td>2012-07-11</td>\n",
       "      <td>2</td>\n",
       "      <td>51.30</td>\n",
       "      <td>Suprêmes délices</td>\n",
       "      <td>Boulevard Tirou, 255</td>\n",
       "      <td>Charleroi</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>B-6000</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id CustomerId  EmployeeId   OrderDate RequiredDate ShippedDate  ShipVia  \\\n",
       "0  10248      VINET           5  2012-07-04   2012-08-01  2012-07-16        3   \n",
       "1  10249      TOMSP           6  2012-07-05   2012-08-16  2012-07-10        1   \n",
       "2  10250      HANAR           4  2012-07-08   2012-08-05  2012-07-12        2   \n",
       "3  10251      VICTE           3  2012-07-08   2012-08-05  2012-07-15        1   \n",
       "4  10252      SUPRD           4  2012-07-09   2012-08-06  2012-07-11        2   \n",
       "\n",
       "   Freight                   ShipName           ShipAddress        ShipCity  \\\n",
       "0    32.38  Vins et alcools Chevalier    59 rue de l'Abbaye           Reims   \n",
       "1    11.61         Toms Spezialitäten         Luisenstr. 48         Münster   \n",
       "2    65.83              Hanari Carnes       Rua do Paço, 67  Rio de Janeiro   \n",
       "3    41.34       Victuailles en stock    2, rue du Commerce            Lyon   \n",
       "4    51.30           Suprêmes délices  Boulevard Tirou, 255       Charleroi   \n",
       "\n",
       "       ShipRegion ShipPostalCode ShipCountry  \n",
       "0  Western Europe          51100      France  \n",
       "1  Western Europe          44087     Germany  \n",
       "2   South America      05454-876      Brazil  \n",
       "3  Western Europe          69004      France  \n",
       "4  Western Europe         B-6000     Belgium  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT * FROM `Order`;\"\"\")\n",
    "df = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## looking at dates to get an idea of timeframe\n",
    "df['OrderDate']=pd.to_datetime(df['OrderDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     830\n",
       "unique                    480\n",
       "top       2014-02-26 00:00:00\n",
       "freq                        6\n",
       "first     2012-07-04 00:00:00\n",
       "last      2014-05-06 00:00:00\n",
       "Name: OrderDate, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OrderDate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timespan is ~ 2 years : April 7 2012 - June 5 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPOTHESIS 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***Does discount amount have a statistically significant effect on the quantity of a product in an order? If so, at what level(s) of discount?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $H_0$:There is no statistcally significant effect on the quantity of a product in an order in relation to a discount amount.\n",
    "- $H_1$:Discounts have a statistically significant effect on the quantiy of a product in an order.\n",
    "- $H_1a$:Certain discount values have a greater effect than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and inspecting data from OrderDetail table:\n",
    "\n",
    "This table from the Northwind database includes order information on:\n",
    "\n",
    "    1) Quantity\n",
    "    2) Discount \n",
    "\n",
    "Once imported, the data will be grouped by discount level and the means of the quantity sold for each will be compared against each other to evaluate if they are statistically significantly different.  This will determine if the null hypothesis can be rejected with a probability of 5% error in reporting a false negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0  10248/11    10248         11       14.0        12       0.0\n",
       "1  10248/42    10248         42        9.8        10       0.0\n",
       "2  10248/72    10248         72       34.8         5       0.0\n",
       "3  10249/14    10249         14       18.6         9       0.0\n",
       "4  10249/51    10249         51       42.4        40       0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT * FROM OrderDetail;\"\"\")\n",
    "df = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2155.000000</td>\n",
       "      <td>2155.000000</td>\n",
       "      <td>2155.000000</td>\n",
       "      <td>2155.000000</td>\n",
       "      <td>2155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10659.375870</td>\n",
       "      <td>40.793039</td>\n",
       "      <td>26.218520</td>\n",
       "      <td>23.812993</td>\n",
       "      <td>0.056167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>241.378032</td>\n",
       "      <td>22.159019</td>\n",
       "      <td>29.827418</td>\n",
       "      <td>19.022047</td>\n",
       "      <td>0.083450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>10248.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>10451.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>10657.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>10862.500000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>11077.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>263.500000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            OrderId    ProductId    UnitPrice     Quantity     Discount\n",
       "count   2155.000000  2155.000000  2155.000000  2155.000000  2155.000000\n",
       "mean   10659.375870    40.793039    26.218520    23.812993     0.056167\n",
       "std      241.378032    22.159019    29.827418    19.022047     0.083450\n",
       "min    10248.000000     1.000000     2.000000     1.000000     0.000000\n",
       "25%    10451.000000    22.000000    12.000000    10.000000     0.000000\n",
       "50%    10657.000000    41.000000    18.400000    20.000000     0.000000\n",
       "75%    10862.500000    60.000000    32.000000    30.000000     0.100000\n",
       "max    11077.000000    77.000000   263.500000   130.000000     0.250000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explorting dataset\n",
    "specs = df.describe()\n",
    "specs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General Observation: Pricing ranges from `$2` - `$263` with an average price of `$26.21` and average qty of 23 items ordered, no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2155 entries, 0 to 2154\n",
      "Data columns (total 6 columns):\n",
      "Id           2155 non-null object\n",
      "OrderId      2155 non-null int64\n",
      "ProductId    2155 non-null int64\n",
      "UnitPrice    2155 non-null float64\n",
      "Quantity     2155 non-null int64\n",
      "Discount     2155 non-null float64\n",
      "dtypes: float64(2), int64(3), object(1)\n",
      "memory usage: 101.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "830"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['OrderId'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Visual Inspection:\n",
    "\n",
    "From this dataset of 2155 line items that span 830 orders, the average quantity ordered is 24 regardless of discount, the minimum ordered is 0 and max ordered is 130, although the IQR is between 10 and 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2155.000000\n",
       "mean       23.812993\n",
       "std        19.022047\n",
       "min         1.000000\n",
       "25%        10.000000\n",
       "50%        20.000000\n",
       "75%        30.000000\n",
       "max       130.000000\n",
       "Name: Quantity, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qty = df['Quantity']\n",
    "qty_specs = qty.describe()\n",
    "qty_specs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average quantity ordered from this sample is : 24.0\n",
      "There are 2155 orders in this sample.\n"
     ]
    }
   ],
   "source": [
    "qty_mu = round(qty_specs['mean'],0)\n",
    "n = len(df)\n",
    "print(f'The average quantity ordered from this sample is : {qty_mu}')\n",
    "print(f'There are {n} orders in this sample.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.15, 0.05, 0.2, 0.25, 0.1, 0.02, 0.03, 0.04, 0.06, 0.01]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d =list(df['Discount'].unique())\n",
    "d\n",
    "#Dscounts are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.distplot(qty)\n",
    "plt.axvline(qty_mu, label='Qty Mean', color='purple')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9cd6cdf8dae4f9184da2fdf74df6817",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='d', options=(0.0, 0.15, 0.05, 0.2, 0.25, 0.1, 0.02, 0.03, 0.04, 0.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "@interact\n",
    "def plt_discounts(d=d):\n",
    "    sns.distplot(df.groupby('Discount').get_group(d)['Quantity'])\n",
    "    plt.axvline(qty_mu, color='purple')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discount\n",
       "0.00    1317\n",
       "0.01       1\n",
       "0.02       2\n",
       "0.03       3\n",
       "0.04       1\n",
       "0.05     185\n",
       "0.06       1\n",
       "0.10     173\n",
       "0.15     157\n",
       "0.20     161\n",
       "0.25     154\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfa = df.groupby('Discount').count()['Quantity']\n",
    "display(dfa)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The sample sizes associated with discounts .01, .02, .03, .04 and .06 are relatively nominal <4, and will be dropped as without a normal or comperable dataset to evaulate their impact in comparison with the other groups.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "discs = {}\n",
    "for disc in df['Discount'].unique():\n",
    "    discs[disc] = df.groupby('Discount').get_group(disc)['Quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.15\n",
      "0.05\n",
      "0.2\n",
      "0.25\n",
      "0.1\n",
      "0.02\n",
      "0.03\n",
      "0.04\n",
      "0.06\n",
      "0.01\n"
     ]
    }
   ],
   "source": [
    "for k,v in discs.items():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[.01,.02,.03,.04,.06]\n",
    "oneper = discs.pop(.01)\n",
    "twoper = discs.pop(.02)\n",
    "threeper = discs.pop(.03)\n",
    "fourper = discs.pop(.04)\n",
    "sixper = discs.pop(.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributions appear roughly equal,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing distributions\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "for k,v in discs.items():\n",
    "    sns.distplot(v,label=v)\n",
    "\n",
    "plt.title('Quantity Distribution')\n",
    "plt.legend()\n",
    "print('Distributions appear roughly equal,')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Observations:\n",
    "\n",
    "Datatype is numeric.  \n",
    "\n",
    "The average quantity ordered from this sample is : 24.0\n",
    "There are 2155 orders in this sample. \n",
    "\n",
    "Discounts are as follows:\n",
    "0.0, 0.15, 0.05, 0.2, 0.25, 0.1, 0.02, 0.03, 0.04, 0.06, 0.01\n",
    "\n",
    "The majority of product purchases are without the discount(1317/2155) 61%, and frequency of discounts are as follows:\n",
    "5%\n",
    "10%\n",
    "20%\n",
    "15%\n",
    "25%\n",
    "\n",
    "For discounts 1%,2%,3%,4%, and 6% , the amount of data provided was relatively small to evaluate the impact on the whole.  This data was removed from further testing.\n",
    "\n",
    "Overall distributions appears relatively uniform. \n",
    "\n",
    "### Since we are comparing multiple discounts to inspect it's impact on quantity ordered an AVNOVA or Kruksal test will be run depending on how assumptions are met: \n",
    "   \n",
    "    \n",
    "Assumptions for ANOVA Testing: \n",
    "(see corresponding sections\n",
    "\n",
    "1) No significant outliers \n",
    "    * Upon a quick visual inspection, the distribution is skewed and visually there appears to be some outliers\n",
    "\n",
    "2) Equal variance\n",
    "    * Lavene's testing demonstrates NOT equal variance\n",
    "\n",
    "\n",
    "3) Normality (if n>15)\n",
    "      * Not required for discounts: 5%, 10%, 15%, 20% and 25% since n > 15\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 1:  Outliers \n",
    "\n",
    "Evaluation and removal via Z-Score testing:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20 outliers in Discount Group 0.0\n",
      "Found 2 outliers in Discount Group 0.15\n",
      "Found 3 outliers in Discount Group 0.05\n",
      "Found 2 outliers in Discount Group 0.2\n",
      "Found 3 outliers in Discount Group 0.25\n",
      "Found 3 outliers in Discount Group 0.1\n",
      "\n",
      " All of these outliers were removed\n"
     ]
    }
   ],
   "source": [
    "for disc, disc_data in discs.items():\n",
    "    idx_outs = fn.find_outliers_Z(disc_data)\n",
    "    print(f'Found {idx_outs.sum()} outliers in Discount Group {disc}')\n",
    "    discs[disc] = disc_data[~idx_outs]\n",
    "print('\\n All of these outliers were removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 2: Equal Variance\n",
    "\n",
    "Levines testing conducted on cleaned dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing data for levene's testing\n",
    "datad = []\n",
    "for k,v in discs.items():\n",
    "    datad.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lavene test for equal variance results are 0.0003\n",
      "The groups do NOT have equal variance\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stat,p = stats.levene(discs[0.0],discs[0.05],discs[0.1],discs[0.25], discs[0.15], discs[0.20])\n",
    "print(f'Lavene test for equal variance results are {round(p,4)}')\n",
    "sig = 'do NOT' if p < .05 else 'DO'\n",
    "\n",
    "print(f'The groups {sig} have equal variance')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since group does not prove to be equal variance, a **kruksal** will be conducted.\n",
    "Looking at sample sizes to determine if normality needs to be tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 3: Normality\n",
    "\n",
    "First, checking sample sizes since assumption for normality depends on sample size. If 2-9 groups, each group n >= 15\n",
    "\n",
    "For Discounts of 5%, 10%, 15%, 20% and 25% n>15 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1297 samples in the 0.0 discount group.\n",
      "There are 155 samples in the 0.15 discount group.\n",
      "There are 182 samples in the 0.05 discount group.\n",
      "There are 159 samples in the 0.2 discount group.\n",
      "There are 151 samples in the 0.25 discount group.\n",
      "There are 170 samples in the 0.1 discount group.\n",
      "\n",
      "All samples are >15: Normality Assumption Criterion is met.\n"
     ]
    }
   ],
   "source": [
    "n = []\n",
    "for disc, disc_data in discs.items():\n",
    "    print(f'There are {len(disc_data)} samples in the {disc} discount group.')    \n",
    "    n.append(len(disc_data)>15)\n",
    "if all(n):\n",
    "    print('\\nAll samples are >15: Normality Assumption Criterion is met.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kruksal Testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal test p value: 0.0\n",
      "Reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.kruskal(discs[0.0],discs[0.05],discs[0.1],discs[0.25], discs[0.15], discs[0.20])\n",
    "print(f\"Kruskal test p value: {round(p,4)}\")\n",
    "if p < .05 :\n",
    "    print(f'Reject the null hypothesis')\n",
    "else: \n",
    "    print(f'Null hypotheis remains true')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANOVA Testing for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA test p value: 0.0\n",
      "Reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.f_oneway(*datad)\n",
    "print(f\"ANOVA test p value: {round(p,4)}\")\n",
    "if p < .05 :\n",
    "    print(f'Reject the null hypothesis')\n",
    "else: \n",
    "    print(f'Null hypotheis remains true')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Hoc Testing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2095</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2096</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2099</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2135</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data group\n",
       "0     12.0   0.0\n",
       "1     10.0   0.0\n",
       "2      5.0   0.0\n",
       "3      9.0   0.0\n",
       "4     40.0   0.0\n",
       "...    ...   ...\n",
       "2095  30.0   0.1\n",
       "2096  77.0   0.1\n",
       "2098  25.0   0.1\n",
       "2099   4.0   0.1\n",
       "2135   2.0   0.1\n",
       "\n",
       "[2114 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc_df = fn.prep_data_for_tukeys(discs)\n",
    "disc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2114 entries, 0 to 2135\n",
      "Data columns (total 2 columns):\n",
      "data     2114 non-null float64\n",
      "group    2114 non-null object\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 49.5+ KB\n"
     ]
    }
   ],
   "source": [
    "disc_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0', '0.15', '0.05', '0.2', '0.25', '0.1']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d =list(disc_df['group'].unique())\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.0</td>   <td>0.05</td>   <td>6.0639</td>   <td>0.001</td> <td>2.4368</td>   <td>9.691</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.0</td>    <td>0.1</td>   <td>2.9654</td>  <td>0.2098</td> <td>-0.7723</td> <td>6.7031</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.0</td>   <td>0.15</td>   <td>6.9176</td>   <td>0.001</td> <td>3.0233</td>  <td>10.8119</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.0</td>    <td>0.2</td>   <td>5.6293</td>   <td>0.001</td> <td>1.7791</td>  <td>9.4796</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.0</td>   <td>0.25</td>   <td>6.1416</td>   <td>0.001</td> <td>2.2016</td>  <td>10.0817</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.05</td>    <td>0.1</td>   <td>-3.0985</td> <td>0.4621</td> <td>-7.9861</td>  <td>1.789</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.05</td>   <td>0.15</td>   <td>0.8537</td>    <td>0.9</td>  <td>-4.1547</td>  <td>5.862</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.05</td>    <td>0.2</td>   <td>-0.4346</td>   <td>0.9</td>  <td>-5.4088</td> <td>4.5396</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.05</td>   <td>0.25</td>   <td>0.0777</td>    <td>0.9</td>  <td>-4.9663</td> <td>5.1218</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.1</td>   <td>0.15</td>   <td>3.9522</td>  <td>0.2311</td> <td>-1.1368</td> <td>9.0412</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.1</td>    <td>0.2</td>   <td>2.6639</td>  <td>0.6409</td> <td>-2.3915</td> <td>7.7193</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.1</td>   <td>0.25</td>   <td>3.1762</td>  <td>0.4872</td> <td>-1.9479</td> <td>8.3004</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.15</td>    <td>0.2</td>   <td>-1.2883</td>   <td>0.9</td>  <td>-6.4605</td>  <td>3.884</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>0.15</td>   <td>0.25</td>   <td>-0.7759</td>   <td>0.9</td>  <td>-6.0154</td> <td>4.4635</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0.2</td>   <td>0.25</td>   <td>0.5123</td>    <td>0.9</td>  <td>-4.6945</td> <td>5.7191</td>   <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sms\n",
    "tukey = sms.stats.multicomp.pairwise_tukeyhsd(disc_df['data'],disc_df['group'])\n",
    "tukey.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a statistically significant effect on quantity purchased based on discount. The discounts below are statistically considered equal:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6.0639</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2.4368</td>\n",
       "      <td>9.6910</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>6.9176</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3.0233</td>\n",
       "      <td>10.8119</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.6293</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.7791</td>\n",
       "      <td>9.4796</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>6.1416</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2.2016</td>\n",
       "      <td>10.0817</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group1 group2  meandiff  p-adj   lower    upper  reject\n",
       "0    0.0   0.05    6.0639  0.001  2.4368   9.6910    True\n",
       "2    0.0   0.15    6.9176  0.001  3.0233  10.8119    True\n",
       "3    0.0    0.2    5.6293  0.001  1.7791   9.4796    True\n",
       "4    0.0   0.25    6.1416  0.001  2.2016  10.0817    True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc_tukey = fn.tukey_df(tukey)\n",
    "disc_tukey_trues = disc_tukey.loc[disc_tukey['reject']==True]\n",
    "disc_tukey_trues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_df['group']=disc_df['group'].astype(str)\n",
    "d=disc_df['group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70b0723be0ab4f349ddc0074181317f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='d', options=('0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "@interact\n",
    "def plt_discounts(d=d):\n",
    "    sns.distplot(disc_df.groupby('group').get_group(d)['data'])\n",
    "    plt.axvline(qty_mu, color='purple')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revised average quantity sold across all orders 23.0\n",
      "Average quantity sold for orders where no discount was extended was: 21.0\n"
     ]
    }
   ],
   "source": [
    "stats = disc_df['data'].describe()\n",
    "dataqty = stats['mean']\n",
    "print(f'Revised average quantity sold across all orders {round(dataqty)}')\n",
    "zeros = disc_df.groupby('group').get_group('0.0')['data']\n",
    "nodiscav = zeros.mean()\n",
    "print(f'Average quantity sold for orders where no discount was extended was: {round(nodiscav)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average quantity sold for 0.0 discount is 21.0\n",
      "The average quantity sold for 0.15 discount is 27.0\n",
      "The average quantity sold for 0.05 discount is 27.0\n",
      "The average quantity sold for 0.2 discount is 26.0\n",
      "The average quantity sold for 0.25 discount is 27.0\n",
      "The average quantity sold for 0.1 discount is 24.0\n"
     ]
    }
   ],
   "source": [
    "for k,v in discs.items():\n",
    "    print(f'The average quantity sold for {k} discount is {round(v.mean())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average qty purchased regardless of discount or none offered is 23.0\n"
     ]
    }
   ],
   "source": [
    "data_mu = disc_df['data'].mean()\n",
    "print(f'The average qty purchased regardless of discount or none offered is {round(data_mu)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Various EDA to understand distributions and remaining potential outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following hex-bin visualization illustrates the density of data in the 0% category. Visually, this appears that equal quantitites are purchased, but might be due to light markings relative to sample sizes.  Clearly not the best choice for EDA, and further exploration is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(336.9714285714286, 0.5, 'Qty')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disc_df['group'] = disc_df['group'].astype(float)\n",
    "sns.jointplot(data=disc_df, x='group', y='data', kind='hex')\n",
    "plt.ylabel('Qty')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual on distributions and potential remaining outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxen plot\n",
    "sns.catplot(data=disc_df, x='group', y='data', kind='boxen')\n",
    "ax.axhline(26.75, color='k')\n",
    "plt.gca()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot may not best suit non-technical audience with the additional information potentially could cause confusion.  However, the above boxen plot clearly illustrates distributions as well as remaining potential outliers in the .05, .15, .2 and .25 groups.  These outliers will not be removed at the present moment with the intention to preserve as much of the initial dataset as possible.  Sample sizes can be referenced under 'Assumption 3: Normality' where this is observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Quantity Purchased')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.barplot(data=disc_df, x='group', y='data', ci=68)\n",
    "#plt.axhline(data_mu, linestyle='--', color='darkgray')\n",
    "plt.axhline(26.75, linestyle='--', color='lightblue')\n",
    "plt.title('Average Quanity Purchased at Varying Discont Levels', fontsize=20)\n",
    "plt.xlabel('Discount Extended')\n",
    "plt.ylabel('Average Quantity Purchased')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effect Sizes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The effect size for 0.0 is 0.0\n",
      "The effect size for 0.15 is 0.453\n",
      "The effect size for 0.05 is 0.3949\n",
      "The effect size for 0.2 is 0.3751\n",
      "The effect size for 0.25 is 0.4109\n",
      "The effect size for 0.1 is 0.1982\n"
     ]
    }
   ],
   "source": [
    "for disc, disc_data in discs.items():\n",
    "    es = fn.Cohen_d(zeros, disc_data)\n",
    "    print(f'The effect size for {disc} is {round(np.abs(es),4)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis 1 Findings and Recommendation:\n",
    "\n",
    "### Findings:\n",
    "**Rejecting the null hypothesis :**\n",
    "*   𝐻0 :There is no statistcally significant effect on the quantity of a product in an order in relation to a discount amount.\n",
    "\n",
    "\n",
    "**Alternative hypothesese:**\n",
    "\n",
    "    *  𝐻1 :Discounts have a statistically significant effect on the quantiy of a product in an order.\n",
    "    *  𝐻1𝑎 :Certain discount values have a greater effect than others.(see below for findings)\n",
    "\n",
    "A 10% discount had no statistical significance on the quantiy purchased.\n",
    "\n",
    "Discounts extended at 5%, 15%, 20%, and 25% statistically are equal and significant in terms of their effect on quantity sold when compared to none offered, with a p-value of .001 meaning there is a .01 percent chance of classifying them as such due to chance. Each have varying effect sizes in compared to orders placed with no discount extended.\n",
    " \n",
    "Discount |AvQty | Effect Size | Effect\n",
    " -- | -- | -- | --|\n",
    "5 % | 27| .1982 | Small\n",
    "15 % | 27| .454 | Medium\n",
    "20 % |26| .3751 | Medium\n",
    "25 % |27 | .454 | Medium\n",
    "\n",
    "Additional notes:\n",
    "For discounts 1%,2%,3%,4%, and 6% that were included in the original dataset, the amount of data provided was relatively small to evaluate the impact on the whole. This data was removed from further testing\n",
    "\n",
    "With additional outliers removed for each of the discount groups, the revised average qty purchased was 23 overall.\n",
    "\n",
    "\n",
    " \n",
    "###  Recommendation:\n",
    "\n",
    "While larger discounts did deomonstrate significant effect on quantity purchased, smaller discounts held a statistically equal effect.  To recognize the effect of driving higher quantities purchased and realize larger profit margins, offer the smaller discounts of 5% and 15%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPOTHESIS 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***Do some categories statistically generate more revenue than others ?? Which ones?***\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- $𝐻0$ : All categories generate equal revenues.\n",
    "- $𝐻1$ : Certain categories sell at statistically higher rates of revneu than others.\n",
    "- $𝐻1𝑎$ : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing and inspecting data from Product and OrderDetail tables:\n",
    "\n",
    "These tables includes product information data including:\n",
    "\n",
    "1) Categories \n",
    "\n",
    "2) Pricing and discount information to generate revenues\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CategoryId</th>\n",
       "      <th>CategoryName</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Queso Cabrales</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>Singaporean Hokkien Fried Mee</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>Mozzarella di Giovanni</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>Tofu</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>Manjimup Dried Apples</td>\n",
       "      <td>42.4</td>\n",
       "      <td>7</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ProductId                    ProductName  UnitPrice  CategoryId  \\\n",
       "0         11                 Queso Cabrales       14.0           4   \n",
       "1         42  Singaporean Hokkien Fried Mee        9.8           5   \n",
       "2         72         Mozzarella di Giovanni       34.8           4   \n",
       "3         14                           Tofu       18.6           7   \n",
       "4         51          Manjimup Dried Apples       42.4           7   \n",
       "\n",
       "     CategoryName  Discount  Quantity  \n",
       "0  Dairy Products       0.0        12  \n",
       "1  Grains/Cereals       0.0        10  \n",
       "2  Dairy Products       0.0         5  \n",
       "3         Produce       0.0         9  \n",
       "4         Produce       0.0        40  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##clean sql notation\n",
    "cur.execute(\"\"\"SELECT \n",
    "                ProductId, \n",
    "                ProductName, \n",
    "                od.UnitPrice,\n",
    "                CategoryID, \n",
    "                CategoryName, \n",
    "                Discount, \n",
    "                Quantity\n",
    "                FROM Product AS p\n",
    "                JOIN OrderDetail as od\n",
    "                ON p.ID = Od.ProductId\n",
    "                JOIN Category as c \n",
    "                ON c.ID = p.CategoryID;\"\"\")\n",
    "catavs = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "catavs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Revenue is calculated by subtracting discounts from the UnitPrice and multiplying by quantity ordered.\n",
    "catavs['Revenue'] = (catavs['UnitPrice'] * (1 - catavs['Discount']))*catavs['Quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "587.0 2\n"
     ]
    }
   ],
   "source": [
    "avcrev = catavs['Revenue'].mean()\n",
    "print (round(avcrev),2)\n",
    "cg = catavs['CategoryName'].unique()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Visual Inspection and Observations:\n",
    "\n",
    "There are 8 different categories sold in this company that represent 77 products.\n",
    "\n",
    "The average revenue generated across all categories is `$587.00`\n",
    "\n",
    "Visually, it appears that there are three categories that significantly generate higher revenues than others, additional testing will demonstrate their siginficance and effect.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.barplot(data=catavs, x='CategoryName', y='Revenue', ci=68, ax=ax)\n",
    "plt.title('Initial Inspection: Average Revenue Generated Accross All Categories', fontsize=18)\n",
    "plt.axhline(avcrev,linestyle=\"--\", color='k', linewidth=.8 )\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8 different categories sold in this company\n",
      "The average revenue generated accross all categories is 587.0\n"
     ]
    }
   ],
   "source": [
    "catcount = len(catavs['CategoryId'].unique())\n",
    "avrev = catavs['Revenue'].mean()\n",
    "print(f'There are {catcount} different categories sold in this company')\n",
    "print(f'The average revenue generated accross all categories is {round(avcrev,0)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cats = {}\n",
    "for cat in catavs['CategoryName'].unique():\n",
    "    cats[cat] = catavs.groupby('CategoryName').get_group(cat)['Revenue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In each of the different categories, the products align as such: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "catsprods = {}\n",
    "for cat in catavs['CategoryName'].unique():\n",
    "    catsprods[cat] = catavs.groupby('CategoryName').get_group(cat)['ProductId'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10 products in the Dairy Products Category\n",
      "There are 7 products in the Grains/Cereals Category\n",
      "There are 5 products in the Produce Category\n",
      "There are 12 products in the Seafood Category\n",
      "There are 12 products in the Condiments Category\n",
      "There are 13 products in the Confections Category\n",
      "There are 12 products in the Beverages Category\n",
      "There are 6 products in the Meat/Poultry Category\n"
     ]
    }
   ],
   "source": [
    "for k,v in catsprods.items():\n",
    "    print(f'There are {len(v)} products in the {k} Category')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 1: Outliers\n",
    "Outliers removed via z-score testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5 outliers in Category # Dairy Products\n",
      "Found 6 outliers in Category # Grains/Cereals\n",
      "Found 1 outliers in Category # Produce\n",
      "Found 8 outliers in Category # Seafood\n",
      "Found 4 outliers in Category # Condiments\n",
      "Found 9 outliers in Category # Confections\n",
      "Found 12 outliers in Category # Beverages\n",
      "Found 4 outliers in Category # Meat/Poultry\n",
      "\n",
      " All of these outliers were removed\n"
     ]
    }
   ],
   "source": [
    "for cat, cat_data in cats.items():\n",
    "    idx_outs = fn.find_outliers_Z(cat_data)\n",
    "    print(f'Found {idx_outs.sum()} outliers in Category # {cat}')\n",
    "    cats[cat] = cat_data[~idx_outs]\n",
    "print('\\n All of these outliers were removed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 77 products\n"
     ]
    }
   ],
   "source": [
    "pids = catavs['ProductName'].unique()\n",
    "pids\n",
    "print(f'There are {len(pids)} products')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 2: Equal Variance\n",
    "\n",
    "Testing cleaned dataset for equal variance.\n",
    "\n",
    "Since the groups do NOT have euqal variance, a Kruksal test will be conducted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "datac = []\n",
    "for k,v in cats.items():\n",
    "    datac.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lavene test for equal variance results are 0.0\n",
      "The groups do NOT have equal variance\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stat,p = stats.levene(*datac)\n",
    "print(f'Lavene test for equal variance results are {round(p,4)}')\n",
    "sig = 'do NOT' if p < .05 else 'DO'\n",
    "\n",
    "print(f'The groups {sig} have equal variance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 3: Normality\n",
    "\n",
    "All groups are > 15 samples = Assumption for normality is met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 361 samples in the data set for Employee #Dairy Products.\n",
      "There are 190 samples in the data set for Employee #Grains/Cereals.\n",
      "There are 135 samples in the data set for Employee #Produce.\n",
      "There are 322 samples in the data set for Employee #Seafood.\n",
      "There are 212 samples in the data set for Employee #Condiments.\n",
      "There are 325 samples in the data set for Employee #Confections.\n",
      "There are 392 samples in the data set for Employee #Beverages.\n",
      "There are 169 samples in the data set for Employee #Meat/Poultry.\n",
      "\n",
      "All samples are >15: Normality Assumption Criterion is met.\n"
     ]
    }
   ],
   "source": [
    "n = []\n",
    "\n",
    "for cat, cat_data in cats.items():\n",
    "    print(f'There are {len(cat_data)} samples in the data set for Employee #{cat}.')\n",
    "    n.append(len(cat_data)>15)\n",
    "if all(n):\n",
    "    print('\\nAll samples are >15: Normality Assumption Criterion is met.')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kruksal Testing:\n",
    "\n",
    "Results: Reject the null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal test p value: 0.0\n",
      "Reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.kruskal(*datac)\n",
    "print(f\"Kruskal test p value: {round(p,4)}\")\n",
    "if p < .05 :\n",
    "    print(f'Reject the null hypothesis')\n",
    "else: \n",
    "    print(f'Null hypotheis remains true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Hoc Testing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>168.00</td>\n",
       "      <td>Dairy Products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>174.00</td>\n",
       "      <td>Dairy Products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>47.50</td>\n",
       "      <td>Dairy Products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1088.00</td>\n",
       "      <td>Dairy Products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>200.00</td>\n",
       "      <td>Dairy Products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2096</td>\n",
       "      <td>2702.70</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2098</td>\n",
       "      <td>738.00</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2099</td>\n",
       "      <td>86.40</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2102</td>\n",
       "      <td>111.75</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2148</td>\n",
       "      <td>48.00</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2106 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         data           group\n",
       "0      168.00  Dairy Products\n",
       "2      174.00  Dairy Products\n",
       "12      47.50  Dairy Products\n",
       "13    1088.00  Dairy Products\n",
       "14     200.00  Dairy Products\n",
       "...       ...             ...\n",
       "2096  2702.70    Meat/Poultry\n",
       "2098   738.00    Meat/Poultry\n",
       "2099    86.40    Meat/Poultry\n",
       "2102   111.75    Meat/Poultry\n",
       "2148    48.00    Meat/Poultry\n",
       "\n",
       "[2106 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df = fn.prep_data_for_tukeys(cats)\n",
    "cat_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visual Inspection Post Data Cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05c4fdb96ef742c5a46a7e36e7d9082f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='d', options=('Dairy Products', 'Grains/Cereals', 'Produce', 'Seafo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "@interact\n",
    "def plt_discounts(d=cg):\n",
    "    sns.distplot(cat_df.groupby('group').get_group(d)['data'])\n",
    "    plt.axvline(cat_df['data'].mean(), color='purple')\n",
    "    plt.title('Average Revenue Generated by Category')\n",
    "    plt.ylabel('Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "      <th>group1</th>         <th>group2</th>     <th>meandiff</th>   <th>p-adj</th>   <th>lower</th>     <th>upper</th>   <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>     <td>Condiments</td>    <td>55.7712</td>    <td>0.9</td>   <td>-78.858</td>  <td>190.4005</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>     <td>Confections</td>   <td>27.0669</td>    <td>0.9</td>  <td>-91.4026</td>  <td>145.5364</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>   <td>Dairy Products</td> <td>194.0913</td>   <td>0.001</td>  <td>78.8965</td>   <td>309.286</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>   <td>Grains/Cereals</td>  <td>21.4034</td>    <td>0.9</td>  <td>-118.1928</td> <td>160.9996</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>    <td>Meat/Poultry</td>   <td>411.042</td>   <td>0.001</td> <td>265.7215</td>  <td>556.3625</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>       <td>Produce</td>    <td>296.1055</td>   <td>0.001</td>  <td>138.516</td>   <td>453.695</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Beverages</td>       <td>Seafood</td>    <td>-46.1557</td>    <td>0.9</td>  <td>-164.9266</td>  <td>72.6151</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>     <td>Confections</td>  <td>-28.7043</td>    <td>0.9</td>  <td>-168.1193</td> <td>110.7106</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>   <td>Dairy Products</td>  <td>138.32</td>   <td>0.0449</td>  <td>1.6769</td>   <td>274.9631</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>   <td>Grains/Cereals</td> <td>-34.3678</td>    <td>0.9</td>  <td>-192.129</td>  <td>123.3933</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>    <td>Meat/Poultry</td>  <td>355.2707</td>   <td>0.001</td> <td>192.4225</td>   <td>518.119</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>       <td>Produce</td>    <td>240.3342</td>   <td>0.001</td>  <td>66.4494</td>  <td>414.2191</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Condiments</td>       <td>Seafood</td>    <td>-101.927</td>  <td>0.3439</td> <td>-241.5981</td>  <td>37.7441</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Confections</td>  <td>Dairy Products</td> <td>167.0244</td>   <td>0.001</td>  <td>46.2712</td>  <td>287.7776</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Confections</td>  <td>Grains/Cereals</td>  <td>-5.6635</td>    <td>0.9</td>  <td>-149.8807</td> <td>138.5537</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Confections</td>   <td>Meat/Poultry</td>  <td>383.9751</td>   <td>0.001</td> <td>234.2101</td>  <td>533.7401</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Confections</td>      <td>Produce</td>    <td>269.0386</td>   <td>0.001</td> <td>107.3415</td>  <td>430.7357</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Confections</td>      <td>Seafood</td>    <td>-73.2226</td>  <td>0.6099</td> <td>-197.392</td>   <td>50.9467</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dairy Products</td> <td>Grains/Cereals</td> <td>-172.6879</td> <td>0.0054</td> <td>-314.2272</td> <td>-31.1485</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dairy Products</td>  <td>Meat/Poultry</td>  <td>216.9507</td>   <td>0.001</td>  <td>69.7626</td>  <td>364.1389</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dairy Products</td>     <td>Produce</td>    <td>102.0142</td>  <td>0.5181</td> <td>-57.2991</td>  <td>261.3276</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dairy Products</td>     <td>Seafood</td>    <td>-240.247</td>   <td>0.001</td> <td>-361.2959</td> <td>-119.1982</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Grains/Cereals</td>  <td>Meat/Poultry</td>  <td>389.6386</td>   <td>0.001</td> <td>222.6607</td>  <td>556.6164</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Grains/Cereals</td>     <td>Produce</td>    <td>274.7021</td>   <td>0.001</td>  <td>96.9438</td>  <td>452.4604</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Grains/Cereals</td>     <td>Seafood</td>    <td>-67.5591</td>   <td>0.831</td> <td>-212.024</td>   <td>76.9057</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>Meat/Poultry</td>      <td>Produce</td>    <td>-114.9365</td> <td>0.5359</td> <td>-297.2246</td>  <td>67.3516</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>Meat/Poultry</td>      <td>Seafood</td>    <td>-457.1977</td>  <td>0.001</td> <td>-607.2012</td> <td>-307.1942</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Produce</td>        <td>Seafood</td>    <td>-342.2612</td>  <td>0.001</td> <td>-504.1792</td> <td>-180.3432</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyc = sms.stats.multicomp.pairwise_tukeyhsd(cat_df['data'],cat_df['group'])\n",
    "tukeyc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average revenue for Dairy Products is $593.86\n",
      "The average revenue for Grains/Cereals is $421.17\n",
      "The average revenue for Produce is $695.87\n",
      "The average revenue for Seafood is $353.61\n",
      "The average revenue for Condiments is $455.54\n",
      "The average revenue for Confections is $426.83\n",
      "The average revenue for Beverages is $399.77\n",
      "The average revenue for Meat/Poultry is $810.81\n"
     ]
    }
   ],
   "source": [
    "for cat, cat_data in cats.items():\n",
    "    print(f'The average revenue for {cat} is ${round(cat_data.mean(),2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 2: A Clean Vizualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(cat_df.groupby('group').mean().sort_values('data').plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Meat/Poultry',\n",
       " 'Produce',\n",
       " 'Dairy Products',\n",
       " 'Condiments',\n",
       " 'Confections',\n",
       " 'Grains/Cereals',\n",
       " 'Beverages',\n",
       " 'Seafood']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = list(cat_df.groupby('group').mean().sort_values('data', ascending=False).index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Revenue* Generated By Category')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "plt.style.use('seaborn')\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.barplot(data=cat_df, x=round(cat_df['data']), y=cat_df['group'],order=index, ci=68, palette='Wistia_r')\n",
    "formatter = ticker.FormatStrFormatter('$%1.2f')\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.axvline(cat_df['data'].mean(), linestyle='--', color='gray')\n",
    "plt.ylabel('Category')\n",
    "plt.xlabel('Average Revenue Generated')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Average Revenue* Generated By Category', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c8c75e4978>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,8))\n",
    "#sns.despine(f, left=True, bottom=True)\n",
    "sns.catplot(data=cat_df, x='group', y='data', ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "tukeycdf = fn.tukey_df(tukeyc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following groups are statistically similar and accept the null hypothesis that all categories generate equal revenue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>55.7712</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-78.8580</td>\n",
       "      <td>190.4005</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Confections</td>\n",
       "      <td>27.0669</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-91.4026</td>\n",
       "      <td>145.5364</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>21.4034</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-118.1928</td>\n",
       "      <td>160.9996</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-46.1557</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-164.9266</td>\n",
       "      <td>72.6151</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Confections</td>\n",
       "      <td>-28.7043</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-168.1193</td>\n",
       "      <td>110.7106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>-34.3678</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-192.1290</td>\n",
       "      <td>123.3933</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-101.9270</td>\n",
       "      <td>0.3439</td>\n",
       "      <td>-241.5981</td>\n",
       "      <td>37.7441</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>-5.6635</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-149.8807</td>\n",
       "      <td>138.5537</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-73.2226</td>\n",
       "      <td>0.6099</td>\n",
       "      <td>-197.3920</td>\n",
       "      <td>50.9467</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Produce</td>\n",
       "      <td>102.0142</td>\n",
       "      <td>0.5181</td>\n",
       "      <td>-57.2991</td>\n",
       "      <td>261.3276</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-67.5591</td>\n",
       "      <td>0.8310</td>\n",
       "      <td>-212.0240</td>\n",
       "      <td>76.9057</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>Produce</td>\n",
       "      <td>-114.9365</td>\n",
       "      <td>0.5359</td>\n",
       "      <td>-297.2246</td>\n",
       "      <td>67.3516</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            group1          group2  meandiff   p-adj     lower     upper  \\\n",
       "0        Beverages      Condiments   55.7712  0.9000  -78.8580  190.4005   \n",
       "1        Beverages     Confections   27.0669  0.9000  -91.4026  145.5364   \n",
       "3        Beverages  Grains/Cereals   21.4034  0.9000 -118.1928  160.9996   \n",
       "6        Beverages         Seafood  -46.1557  0.9000 -164.9266   72.6151   \n",
       "7       Condiments     Confections  -28.7043  0.9000 -168.1193  110.7106   \n",
       "9       Condiments  Grains/Cereals  -34.3678  0.9000 -192.1290  123.3933   \n",
       "12      Condiments         Seafood -101.9270  0.3439 -241.5981   37.7441   \n",
       "14     Confections  Grains/Cereals   -5.6635  0.9000 -149.8807  138.5537   \n",
       "17     Confections         Seafood  -73.2226  0.6099 -197.3920   50.9467   \n",
       "20  Dairy Products         Produce  102.0142  0.5181  -57.2991  261.3276   \n",
       "24  Grains/Cereals         Seafood  -67.5591  0.8310 -212.0240   76.9057   \n",
       "25    Meat/Poultry         Produce -114.9365  0.5359 -297.2246   67.3516   \n",
       "\n",
       "    reject  \n",
       "0    False  \n",
       "1    False  \n",
       "3    False  \n",
       "6    False  \n",
       "7    False  \n",
       "9    False  \n",
       "12   False  \n",
       "14   False  \n",
       "17   False  \n",
       "20   False  \n",
       "24   False  \n",
       "25   False  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeycfalse  = tukeycdf.loc[tukeycdf['reject']==False]\n",
    "tukeycfalse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The folowing groups can reject the null hypothesis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>194.0913</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>78.8965</td>\n",
       "      <td>309.2860</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>411.0420</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>265.7215</td>\n",
       "      <td>556.3625</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Produce</td>\n",
       "      <td>296.1055</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>138.5160</td>\n",
       "      <td>453.6950</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>138.3200</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>1.6769</td>\n",
       "      <td>274.9631</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>355.2707</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>192.4225</td>\n",
       "      <td>518.1190</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Produce</td>\n",
       "      <td>240.3342</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>66.4494</td>\n",
       "      <td>414.2191</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>167.0244</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>46.2712</td>\n",
       "      <td>287.7776</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>383.9751</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>234.2101</td>\n",
       "      <td>533.7401</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Produce</td>\n",
       "      <td>269.0386</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>107.3415</td>\n",
       "      <td>430.7357</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>-172.6879</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-314.2272</td>\n",
       "      <td>-31.1485</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>216.9507</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>69.7626</td>\n",
       "      <td>364.1389</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-240.2470</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-361.2959</td>\n",
       "      <td>-119.1982</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>389.6386</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>222.6607</td>\n",
       "      <td>556.6164</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>Produce</td>\n",
       "      <td>274.7021</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>96.9438</td>\n",
       "      <td>452.4604</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-457.1977</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-607.2012</td>\n",
       "      <td>-307.1942</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Produce</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>-342.2612</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-504.1792</td>\n",
       "      <td>-180.3432</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            group1          group2  meandiff   p-adj     lower     upper  \\\n",
       "2        Beverages  Dairy Products  194.0913  0.0010   78.8965  309.2860   \n",
       "4        Beverages    Meat/Poultry  411.0420  0.0010  265.7215  556.3625   \n",
       "5        Beverages         Produce  296.1055  0.0010  138.5160  453.6950   \n",
       "8       Condiments  Dairy Products  138.3200  0.0449    1.6769  274.9631   \n",
       "10      Condiments    Meat/Poultry  355.2707  0.0010  192.4225  518.1190   \n",
       "11      Condiments         Produce  240.3342  0.0010   66.4494  414.2191   \n",
       "13     Confections  Dairy Products  167.0244  0.0010   46.2712  287.7776   \n",
       "15     Confections    Meat/Poultry  383.9751  0.0010  234.2101  533.7401   \n",
       "16     Confections         Produce  269.0386  0.0010  107.3415  430.7357   \n",
       "18  Dairy Products  Grains/Cereals -172.6879  0.0054 -314.2272  -31.1485   \n",
       "19  Dairy Products    Meat/Poultry  216.9507  0.0010   69.7626  364.1389   \n",
       "21  Dairy Products         Seafood -240.2470  0.0010 -361.2959 -119.1982   \n",
       "22  Grains/Cereals    Meat/Poultry  389.6386  0.0010  222.6607  556.6164   \n",
       "23  Grains/Cereals         Produce  274.7021  0.0010   96.9438  452.4604   \n",
       "26    Meat/Poultry         Seafood -457.1977  0.0010 -607.2012 -307.1942   \n",
       "27         Produce         Seafood -342.2612  0.0010 -504.1792 -180.3432   \n",
       "\n",
       "    reject  \n",
       "2     True  \n",
       "4     True  \n",
       "5     True  \n",
       "8     True  \n",
       "10    True  \n",
       "11    True  \n",
       "13    True  \n",
       "15    True  \n",
       "16    True  \n",
       "18    True  \n",
       "19    True  \n",
       "21    True  \n",
       "22    True  \n",
       "23    True  \n",
       "26    True  \n",
       "27    True  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyctrues  = tukeycdf.loc[tukeycdf['reject']==True]\n",
    "tukeyctrues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mult_Cohn_d(tukey_result_df, df_dict):\n",
    "    '''Using a dataframe from Tukey Test Results and a \n",
    "    corresponding dictionary, this function loops through \n",
    "    each variable and returns the adjusted p-value and Cohn_d test'''\n",
    "    import pandas as pd\n",
    "    \n",
    "    res = [['g1', 'g2','padj', 'd']]\n",
    "    for i, row in tukey_result_df.iterrows():\n",
    "        g1 = row['group1']\n",
    "        g2 = row['group2']\n",
    "        padj = row['p-adj']\n",
    "        d = fn.Cohen_d(df_dict[g1], df_dict[g2])\n",
    "\n",
    "        res.append([g1, g2,padj, d])\n",
    "\n",
    "    mdc = pd.DataFrame(res[1:], columns=res[0])\n",
    "    return mdc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table below illustrates those categories that can reject the null hypothesis that states all categories generate equal revenue.  The padj is the probability this is due to chance and the d column shows the effect size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
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       "      <th>d</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0010</td>\n",
       "      <td>-0.351635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.596175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.520603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>-0.277491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.517399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Condiments</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.490099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Confections</td>\n",
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       "      <td>0.0010</td>\n",
       "      <td>-0.340630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.600173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Confections</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.560435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.349940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.310545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Dairy Products</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.525083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.563832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Grains/Cereals</td>\n",
       "      <td>Produce</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.570886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Meat/Poultry</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.754594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Produce</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.798069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                g1              g2    padj         d\n",
       "0        Beverages  Dairy Products  0.0010 -0.351635\n",
       "1        Beverages    Meat/Poultry  0.0010 -0.596175\n",
       "2        Beverages         Produce  0.0010 -0.520603\n",
       "3       Condiments  Dairy Products  0.0449 -0.277491\n",
       "4       Condiments    Meat/Poultry  0.0010 -0.517399\n",
       "5       Condiments         Produce  0.0010 -0.490099\n",
       "6      Confections  Dairy Products  0.0010 -0.340630\n",
       "7      Confections    Meat/Poultry  0.0010 -0.600173\n",
       "8      Confections         Produce  0.0010 -0.560435\n",
       "9   Dairy Products  Grains/Cereals  0.0054  0.349940\n",
       "10  Dairy Products    Meat/Poultry  0.0010 -0.310545\n",
       "11  Dairy Products         Seafood  0.0010  0.525083\n",
       "12  Grains/Cereals    Meat/Poultry  0.0010 -0.563832\n",
       "13  Grains/Cereals         Produce  0.0010 -0.570886\n",
       "14    Meat/Poultry         Seafood  0.0010  0.754594\n",
       "15         Produce         Seafood  0.0010  0.798069"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_Cohn_d(tukeyctrues, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 2 Findings and Recommendation:\n",
    "\n",
    "#### Findings:\n",
    "\n",
    "***Reject the null hypothesis:***\n",
    "* 𝐻0  : All categories generate equal revenues.\n",
    "\n",
    "***Alternative hypotheis***\n",
    "* 𝐻1  : Certain categories sell at statistically higher rates of revnue than others.\n",
    "\n",
    "Top three revenue-generating categories: Meat/Poultry, Produce, and Dairy Products.\n",
    "\n",
    "Statistically, Meat/Poultry & Produce were significant and found to be statistically equivalent with p-values of .001 when compared to all other categories. Dairy & Produce were also satistically equal in performance and significant in comparison with the majority of the other product categories.\n",
    "\n",
    "(Definition of statistically equal: they returned false value from Tukey test, indicating they had a simliar mean and therefore statiticaly equal with a .05 chance of falsely being classified as such)\n",
    "\n",
    "\n",
    " \n",
    " \n",
    "Category | Average Revenue\n",
    "-- | -- |\n",
    "Meat/Poultry | `$810.81`\n",
    "Produce | `$695.87`\n",
    "Dairy Products | `$593.86`\n",
    "Condiments | `$455.54`\n",
    "Confections | `$426.83`\n",
    "Grains/Cereals | `$421.17``\n",
    "Beverages | `$399.77`\n",
    "Seafood | `$353.61`\n",
    "\n",
    "The table(s) above outlines how the various categories compare to each other.  If the adjusted p-value in column padj is >.05, they are statistically equal.  Conversely of the adjusted p-value 'adjp' is <.05 the two samples are statistically not-equal.  This is further examined by the effect size illustrated in column d.   d=0.2 be considered a 'small' effect size, 0.5 represents a 'medium' effect size and 0.8 a 'large' effect size. \n",
    "\n",
    "Notes:\n",
    "Revenue is calculated by subtracting any extended discounts from the salesprice and multiplying that by quantity sold.\n",
    "\n",
    "\n",
    "#### Recommendation: \n",
    "\n",
    "If there are additional products that align with the higher revenue generating categories, that category could be broadened to maximize revenue generating potential.  \n",
    "\n",
    "Example: Meat/Poultry currently has 6 products, this could be expanded.  Conversely, the seafood category carries 12 products which could be narrowed.  Additional analysis could demonstrate which seafood are the best sellers which would be preserved.\n",
    "\n",
    "Knowing what revenue each category generates could potentially influence the ability to appropriately categorize discounts.  However, not knowing profit margins - we'd need to take this into consideration.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPOTHESIS 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***Do certain sales representatives statistically sell more than others? Who are the top sellers?***\n",
    "\n",
    "$H0$: All sales representatives generate equal revenue.\n",
    "\n",
    "$H1$: Some sales representatives generate more than others in revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing and inspecting data from Product, OrderDetail, Order and Employee Tables\n",
    "These table includes information on:\n",
    "\n",
    "    1) Product information including SalesPrice, Discount and Quantity Sold\n",
    "    2) Sales Representative Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Discontinued</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>SalesPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>EmployeeId</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Region</th>\n",
       "      <th>ShippedDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Queso Cabrales</td>\n",
       "      <td>0</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.00</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>2012-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>Singaporean Hokkien Fried Mee</td>\n",
       "      <td>1</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.80</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>2012-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>Mozzarella di Giovanni</td>\n",
       "      <td>0</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.80</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>2012-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>Tofu</td>\n",
       "      <td>0</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.60</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>Suyama</td>\n",
       "      <td>Michael</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>2012-07-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>Manjimup Dried Apples</td>\n",
       "      <td>0</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.40</td>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>Suyama</td>\n",
       "      <td>Michael</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>2012-07-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2150</td>\n",
       "      <td>64</td>\n",
       "      <td>Wimmers gute Semmelknödel</td>\n",
       "      <td>0</td>\n",
       "      <td>11077</td>\n",
       "      <td>64</td>\n",
       "      <td>33.25</td>\n",
       "      <td>2</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>North America</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2151</td>\n",
       "      <td>66</td>\n",
       "      <td>Louisiana Hot Spiced Okra</td>\n",
       "      <td>0</td>\n",
       "      <td>11077</td>\n",
       "      <td>66</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>North America</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2152</td>\n",
       "      <td>73</td>\n",
       "      <td>Röd Kaviar</td>\n",
       "      <td>0</td>\n",
       "      <td>11077</td>\n",
       "      <td>73</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>North America</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2153</td>\n",
       "      <td>75</td>\n",
       "      <td>Rhönbräu Klosterbier</td>\n",
       "      <td>0</td>\n",
       "      <td>11077</td>\n",
       "      <td>75</td>\n",
       "      <td>7.75</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>North America</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2154</td>\n",
       "      <td>77</td>\n",
       "      <td>Original Frankfurter grüne Soße</td>\n",
       "      <td>0</td>\n",
       "      <td>11077</td>\n",
       "      <td>77</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>North America</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2155 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId                      ProductName  Discontinued  OrderId  \\\n",
       "0            11                   Queso Cabrales             0    10248   \n",
       "1            42    Singaporean Hokkien Fried Mee             1    10248   \n",
       "2            72           Mozzarella di Giovanni             0    10248   \n",
       "3            14                             Tofu             0    10249   \n",
       "4            51            Manjimup Dried Apples             0    10249   \n",
       "...         ...                              ...           ...      ...   \n",
       "2150         64        Wimmers gute Semmelknödel             0    11077   \n",
       "2151         66        Louisiana Hot Spiced Okra             0    11077   \n",
       "2152         73                       Röd Kaviar             0    11077   \n",
       "2153         75             Rhönbräu Klosterbier             0    11077   \n",
       "2154         77  Original Frankfurter grüne Soße             0    11077   \n",
       "\n",
       "      ProductId  SalesPrice  Quantity  Discount  EmployeeId  LastName  \\\n",
       "0            11       14.00        12      0.00           5  Buchanan   \n",
       "1            42        9.80        10      0.00           5  Buchanan   \n",
       "2            72       34.80         5      0.00           5  Buchanan   \n",
       "3            14       18.60         9      0.00           6    Suyama   \n",
       "4            51       42.40        40      0.00           6    Suyama   \n",
       "...         ...         ...       ...       ...         ...       ...   \n",
       "2150         64       33.25         2      0.03           1   Davolio   \n",
       "2151         66       17.00         1      0.00           1   Davolio   \n",
       "2152         73       15.00         2      0.01           1   Davolio   \n",
       "2153         75        7.75         4      0.00           1   Davolio   \n",
       "2154         77       13.00         2      0.00           1   Davolio   \n",
       "\n",
       "     FirstName         Region ShippedDate  \n",
       "0       Steven  British Isles  2012-07-16  \n",
       "1       Steven  British Isles  2012-07-16  \n",
       "2       Steven  British Isles  2012-07-16  \n",
       "3      Michael  British Isles  2012-07-10  \n",
       "4      Michael  British Isles  2012-07-10  \n",
       "...        ...            ...         ...  \n",
       "2150     Nancy  North America        None  \n",
       "2151     Nancy  North America        None  \n",
       "2152     Nancy  North America        None  \n",
       "2153     Nancy  North America        None  \n",
       "2154     Nancy  North America        None  \n",
       "\n",
       "[2155 rows x 13 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT \n",
    "                ProductID, \n",
    "                ProductName, \n",
    "                Discontinued,\n",
    "                OrderID, \n",
    "                ProductID, \n",
    "                od.UnitPrice AS SalesPrice, \n",
    "                Quantity, \n",
    "                Discount,\n",
    "                EmployeeID, \n",
    "                LastName, \n",
    "                FirstName, \n",
    "                e.Region, \n",
    "                ShippedDate\n",
    "                FROM Product AS p\n",
    "                JOIN OrderDetail AS od\n",
    "                ON od.ProductID = p.Id \n",
    "                JOIN 'Order' AS o\n",
    "                ON o.Id = od.OrderId\n",
    "                JOIN Employee AS e\n",
    "                ON o.EmployeeID = e.ID;\"\"\")\n",
    "dfr = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "dfr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Sales Revenue is calculated by multiplying the adjusted price (accounting for any discounts) times quantity\n",
    "dfr['SaleRev'] = (dfr['SalesPrice'] * (1-dfr['Discount'])) * dfr['Quantity']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9 employees in this company associated with sales information\n",
      "The calculated avarage revenue generated by a sales representative in this dataset is $587.0\n"
     ]
    }
   ],
   "source": [
    "empcount = len(dfr['EmployeeId'].unique())\n",
    "avrev = dfr['SaleRev'].mean()\n",
    "print(f'There are {empcount} employees in this company associated with sales information')\n",
    "print(f'The calculated avarage revenue generated by a sales representative in this dataset is ${round(avrev)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 3 Preliminary Visualizations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "sns.distplot(dfr['SaleRev'], color='green')\n",
    "plt.axvline(avrev, color='lightgreen' )\n",
    "plt.xlabel('Sales Revenue')\n",
    "plt.ylabel('Distribution')\n",
    "plt.title('Average Sales Revenue Distribution', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "sns.barplot(data=dfr, x='EmployeeId', y='SaleRev', ci=68, ax=ax)\n",
    "plt.title('Average Revenue by Sales Representative', fontsize=16)\n",
    "plt.axhline(avrev,linestyle=\"--\", color='gray', linewidth=.6 )\n",
    "plt.xlabel('Employee Id')\n",
    "plt.ylabel('Sales Revenue')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees are listed in the table below:\n",
    "Although the employee names are unique, for the sake of data inspection we'll continue to use Employee Id as the unique identifier and reference the table above to gather additional insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Title</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Fuller</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>Vice President, Sales</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Leverling</td>\n",
       "      <td>Janet</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Peacock</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>British Isles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Suyama</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>British Isles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>King</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>British Isles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Callahan</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Inside Sales Coordinator</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Dodsworth</td>\n",
       "      <td>Anne</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>British Isles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   LastName FirstName                     Title         Region\n",
       "0   1    Davolio     Nancy      Sales Representative  North America\n",
       "1   2     Fuller    Andrew     Vice President, Sales  North America\n",
       "2   3  Leverling     Janet      Sales Representative  North America\n",
       "3   4    Peacock  Margaret      Sales Representative  North America\n",
       "4   5   Buchanan    Steven             Sales Manager  British Isles\n",
       "5   6     Suyama   Michael      Sales Representative  British Isles\n",
       "6   7       King    Robert      Sales Representative  British Isles\n",
       "7   8   Callahan     Laura  Inside Sales Coordinator  North America\n",
       "8   9  Dodsworth      Anne      Sales Representative  British Isles"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT ID, LastName, FirstName, Title, Region\n",
    "                from Employee\"\"\")\n",
    "empsdata = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "empsdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "reps = {}\n",
    "for rep in dfr['EmployeeId'].unique():\n",
    "    reps[str(rep)] = dfr.groupby('EmployeeId').get_group(rep)['SaleRev']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributions appear roughly equal and there appears to be outliers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "for k,v in reps.items():\n",
    "    sns.distplot(v,label=v)\n",
    "\n",
    "plt.title('Sales Revenue By Distribution by Rep')\n",
    "print('Distributions appear roughly equal and there appears to be outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial Observations:\n",
    "Datatype is numeric in this 2155 order sample.\n",
    "\n",
    "There are 9 employees in this company associated with sales information\n",
    "\n",
    "The avarage revenue generated by a sales representative is $629.00. \n",
    "\n",
    "Initial visual inspection indicates roughly uniform distribution in sales revenue, more than half of the sales representatives achieve the average.  Additional testing will demonstrate if it is significant.\n",
    "\n",
    "Since we are comparing multiple discounts to inspect it's impact on quantity ordered an AVNOVA test will be run:\n",
    "Assumptions for ANOVA Testing: \n",
    "\n",
    "1) No significant outliers \n",
    "    Upon a quick visual inspection, there appears to be some outliers that could be removed\n",
    "\n",
    "2) Equal variance\n",
    "    \n",
    "\n",
    "\n",
    "3) Normality (if n>15)\n",
    "      Not required for samples greater than 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 1: Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3 outliers in Employee # 5\n",
      "Found 3 outliers in Employee # 6\n",
      "Found 7 outliers in Employee # 4\n",
      "Found 4 outliers in Employee # 3\n",
      "Found 2 outliers in Employee # 9\n",
      "Found 4 outliers in Employee # 1\n",
      "Found 5 outliers in Employee # 8\n",
      "Found 5 outliers in Employee # 2\n",
      "Found 5 outliers in Employee # 7\n",
      "\n",
      " All of these outliers were removed\n"
     ]
    }
   ],
   "source": [
    "for rep, rep_data in reps.items():\n",
    "    idx_outs = fn.find_outliers_Z(rep_data)\n",
    "    print(f'Found {idx_outs.sum()} outliers in Employee # {rep}')\n",
    "    reps[rep] = rep_data[~idx_outs]\n",
    "print('\\n All of these outliers were removed')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributions appear roughly equal and outliers are visibly removed in comparisson with other visual\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "for k,v in reps.items():\n",
    "    sns.distplot(v,label=v)\n",
    "\n",
    "plt.title('Revised: Sales Revenue Distribution by Rep')\n",
    "print('Distributions appear roughly equal and outliers are visibly removed in comparisson with other visual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Asumption 2: Equal Variance\n",
    "\n",
    "Results are NOT equal variance for this group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from functions import test_equal_variance\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "datas = []\n",
    "for k,v in reps.items():\n",
    "    datas.append(v)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lavene test for equal variance results are 0.0143\n",
      "The groups do NOT have equal variance\n"
     ]
    }
   ],
   "source": [
    "stat,p = stats.levene(*datas)\n",
    "print(f'Lavene test for equal variance results are {round(p,4)}')\n",
    "sig = 'do NOT' if p < .05 else 'DO'\n",
    "\n",
    "print(f'The groups {sig} have equal variance')\n",
    "      \n",
    "          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assumption 3:  Normality\n",
    "The lengths of these samples are >15 so normality criteria is met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 114 samples in the data set for Employee #5.\n",
      "There are 165 samples in the data set for Employee #6.\n",
      "There are 413 samples in the data set for Employee #4.\n",
      "There are 317 samples in the data set for Employee #3.\n",
      "There are 105 samples in the data set for Employee #9.\n",
      "There are 341 samples in the data set for Employee #1.\n",
      "There are 255 samples in the data set for Employee #8.\n",
      "There are 236 samples in the data set for Employee #2.\n",
      "There are 171 samples in the data set for Employee #7.\n",
      "\n",
      "All samples are >15: Normality Assumption Criterion is met.\n"
     ]
    }
   ],
   "source": [
    "n = []\n",
    "\n",
    "for rep,samples in reps.items():\n",
    "    print(f'There are {len(samples)} samples in the data set for Employee #{rep}.')\n",
    "    n.append(len(samples)>15)\n",
    "if all(n):\n",
    "    print('\\nAll samples are >15: Normality Assumption Criterion is met.')\n",
    "    \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal test p value: 0.2968\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.kruskal(*datas)\n",
    "print(f\"Kruskal test p value: {round(p,4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.180626766904163 0.026222185205141618\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.f_oneway(*datas)\n",
    "print(stat,p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>168.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>98.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>174.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>45.900</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>342.720</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2077</td>\n",
       "      <td>390.000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2103</td>\n",
       "      <td>52.350</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2104</td>\n",
       "      <td>386.400</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2105</td>\n",
       "      <td>490.000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2123</td>\n",
       "      <td>232.085</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2117 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         data group\n",
       "0     168.000     5\n",
       "1      98.000     5\n",
       "2     174.000     5\n",
       "17     45.900     5\n",
       "18    342.720     5\n",
       "...       ...   ...\n",
       "2077  390.000     7\n",
       "2103   52.350     7\n",
       "2104  386.400     7\n",
       "2105  490.000     7\n",
       "2123  232.085     7\n",
       "\n",
       "[2117 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data = fn.prep_data_for_tukeys(reps)\n",
    "clean_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 3: A Clean Visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Revenue Generated by Sales Representative')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SKbduVWxVGWlMTAxycnLw+eefo02bNtx6ampq3CWe/fv3y8U+YcIE3raq+lZ+//79OrX9ZRUVFYiOjoaFhQVGjRrFW/bxxx/DwcEBJ06cqLX3oWp/GhgYyC2TyWRYt25dtf+q7Nu3DwAwb948qKv/b5xu27Zt4e/vj6ysLN5lMABcN2cVfX192Nvbo6ysDJmZmQBeb59VMTMzk/um5+rqioULF3Lfpqu0atUKbdu2VdjF/fTpU8THx8PDwwOenp68ZaNHj0arVq24ruGsrCxcuHABbm5uvG/VJiYmrz1Y+IMPPuDtUy0tLXz99dcA/tcjoKurCy8vL9y7d4/XBfzHH39AKpXW6fatQYMG4cCBA/D19YW+vj7Ky8tx5coVbNy4Eb6+vpg8eTJvG7Vs2RLLly/HtGnTePWoq6uja9euAGofcLZv3z7o6+vLnTO6dOmCvn374vr16/j7778BVHZ3l5aW8tqmp6eHffv24dSpU7zn1yYgIIB3vujcuTNcXV0RGhqKZs2a4bvvvkP79u15MQKvd0zb29vzemOaNGmCL774AhUVFXU67ypjuyjrvVmTsWPHYuXKlbxtCYAb0PkmAxF9fHwgk8l4lxwePXqE5ORkfPzxx9DR0eHaWtftV5MGu8ugqnt8wIABvHJvb2/88ssviIqKwtixYwFUdmU6ODjg9OnTKC4uhq6uLpKTk5GRkcH7cElJSQFQed0xLi6OV++TJ08AVHZnvqy6bhpHR0c4OjoiLy8Pt27dwsOHD5Geno6rV6+irKyM+5AsKyvD7du3YWVlBX19fV4durq6sLS0RFJSElf2JvHV5OXbDouKinDs2DE8efIEPj4+WLx4MbS0tHjr37hxA0Dl9ehXD4qqLtKXr+MPHDgQmzZtQlJSElxcXCCRSHDy5EnY2trCzMyMV2dKSkq13YJCoVBubICmpiZatWrFK6tpvENdpKen48WLF5BKpdXGULW/0tLSuJP1q6oSt4KCArllMpkM69evr/Z5VaOBU1JSoKmpiV9++aXa+IDK/frRRx9x5R9++KHculXHUNU18tfdZwB4iVmVqi7Z4uJiJCcn48GDB7h//z6uX7+OBw8e8JK+6ty8eROMMeTl5VW7jTU0NJCZmYmsrCwunlfHWADgnWTrwtbWFkKhkFdmZWUFNTU1XrsHDx6M3377Db///js6d+4MoDKZMjY2hpubW51eSywWY/ny5aioqMD169dx7tw5xMfH48qVKzh16hSeP3+OX3/9FQKBAC1btoSvry8qKiqQkpKC9PR0PHz4ELdu3eI+XGQyWbWvU1xcjPT0dO5D+FXPnj0DUHm8WFhYwM/PD2FhYRgzZgwsLS3h7u4ODw8PdO3aFWpqdf8+V3XbIWMMWVlZOHLkCCQSCWbPno2AgAC5xOJNjunq7mKourT46nH66nlXWdtFWe/NmlQdb9nZ2UhNTeU+O6ouvyp6r1Wnd+/eWLhwIY4cOcIlyH/88QeAymQBeP3tV5MGSQiKiopw8uRJAKjxetudO3dw5coV7iTi4+ODS5cuITY2FgMHDuQ2SNW3VaDyWjIAbN68ucbXzs/P5/1d3QCy/Px8LFu2DIcPH0Z5eTkEAgFat24NZ2dnXkZaNUiqplu0mjdvzvv7TeKryau3HU6bNg0TJ07EoUOHoK+vL3f9vuq1a7sN8eXXHjRoEDZt2oSjR4/CxcUF8fHxKCgoqHZ7V+2LurSnaqzHy6pORuwNBklVfYjfu3evxg/u6uJ4WatWrSAUCpGZmQmJRMKLUV1dHWlpabz1Bw0axDvBFRYWoqKi4rVevy7b4XX3GVD98VxWVobVq1djz5493MCoFi1aoFu3bjA2NkZ2dnaN9QP/28ZXr16tdcKrvLw8bl1dXV255a/2mClS3b3YGhoa0NTU5F3nd3Z2RsuWLXHkyBHMnj0b6enpSElJwejRo6GhofFar6murs5NRjV58mRcuXIFkydPxtWrV5GYmAhnZ2cAlftkw4YN3PgUAwMD2Nraon379khOTq7xWK7qqcrOzq7T8eLv748mTZpg+/btuHTpEtLS0vDDDz+gRYsWCAkJ4cZdKfLqbYcTJ07EyJEjsXz5cjRr1kyunjc5pl893wH/Oze+2kP36nGqrO2irPdmTTIzM7F48WLExMSAMQY1NTV8+OGH6Nq1q9wgxrrS1dVFz549ceTIEWRlZaFFixY4cuQImjRpwvViv+72q0mDJATHjh1DaWkpunTpwmX0L0tPT0dSUhKioqK4hKBfv35YsmQJjh49igEDBuDYsWMQi8W8Ufw6OjoQCoVITk5+7RPBy2bNmsUNlhs0aBDEYjH3LfblwUtVJ72auqOLi4t5f9dXfNXR0dHBmjVrMGjQIPzyyy8Qi8Xw9/fnLQeAkydPom3btgrra9euHaytrfHnn38iLCwMR48ehVAo5J04qurcunWr3KCr+lDdm+/VgaVV+2DQoEFYsWLFG72OtrY2/vOf/+DMmTM4e/asXLe4Ijo6OtDV1ZXr9Xlbr7vParJ8+XLs2rULffr0wahRo2BpaQkjIyMAle8rRQlBVRyTJ0+W6yZ/VVVPV1Uy87JXB+spUl2PTVFREUpKSnjJhZqaGnx8fLB582ZcuXKF+5Zel8sFkydPxrVr1xATE1PtB4G9vT3Gjh2L1atX48GDB3B2dsbRo0cRFhYGS0tLhIWFwcrKiuv1CgsLQ3Jyco2vV7UtHR0dq/3WWh0vLy94eXmhoKAAiYmJiImJwe+//44ZM2agQ4cOEIvFdarnZWZmZli1ahXGjRuH4OBgmJub8y41vckxXVZWJldWtQ+rjreaKGu7KOu9WR3GGCZOnIg7d+7giy++QK9evWBhYQEtLS08e/as1rtdFPHx8cEff/yBY8eOwd3dHbdu3cKYMWO4yyBvsv2q0yBjCKouF4SEhGDRokVy/yIjI6GmpoajR49yH7aGhobw8PDA2bNnce7cOWRlZfG+rQKVt2tIpdJqu92vXLmCVatW4eLFi7XGVlBQgNOnT8Pa2hoLFy6Eg4MDlwxkZGSgrKyM+6DS09PDhx9+iNTUVLnubqlUynX51md8tWnatCnCw8MBVH4IZGRk8F4bAK5fvy73vPv37yMyMhIxMTG8ch8fH+Tl5eH8+fOIiYnBf/7zH+4WpZfrfLWdQOW3xYiIiDe+c0JDQwOlpaVyScHDhw95f7dr1w4ikQgpKSnVJhBbt27Fxo0bkZubW+vrjRw5EgDwzTffVHtie9mr3cGWlpZ48uRJtR+ssbGx+O9//yvXZVoXb7LPqnP48GE0adIEa9euRffu3bmTc2lpKR4/fgyg9m8+te1nAPj222+xefNmSCQSdO7cGQKBAJcvX5Zbr6bn16S6dlfVa2VlxSuvGh8RGxuLuLg4tGvXjuuuro2Ghgays7MRHx+vcN2qb8CHDx8GUHms9OrVi3cJ7N69ewBq3p76+vowNTXFnTt35JJboPLOq3Xr1iEjIwMSiQSbNm3C1q1bAVT2Qnh5eWHZsmWYNGkSZDIZrly5ojDumri4uGD06NHcpYOqWzCBNzumq9tfVT1Ktra2tcairO2irPdmddLS0nD79m14eXnh66+/RpcuXbhLt3fv3gXAPy7qOv4DqBwHZGJigpiYGO6W45c//15n+9VG5QnBo0ePcOHCBbRu3brGa7qmpqZwdnbGixcveAMJfXx8UFJSgsjISAgEArmEoOo2jqVLl/K+tRcVFSE8PBw//PCDwms4GhoaUFNTQ0FBAe9DvrS0FIsXLwbAv470ySefoKioSO7a6vfffy93ENZHfIp4eXmhd+/eKCkp4ZIDoHLbCYVCrFmzhhdXRUUFFi9ejJ9++knuPvGBAwdCXV0dK1aswIsXL7jrVS+/lp6eHn788UfuelyVlStXYvv27XIf4HVlbm6OiooK3ok6Ly9PLvvV1NRE//79cefOHfz888+8ZYmJiVixYgX279+vsLu6Z8+e+OSTT/D3339j/PjxePTokdw6T58+xbx583D79m3em9nX1xeMMSxevJh3zDx9+hTh4eHYvHkzl8G/jjfZZ9XR1NREWVkZ7xu3VCpFREQEd/J4+ZjW0NDg/d22bVt069YN8fHxcvMfREdHY8OGDdxtm82aNYObmxvOnz/PGwRVVFRUa1dmdW7fvs0bjFZUVIQ1a9ZAIBDgk08+4a3bvn17WFtb4/fff8eNGzfqPBd81TXZ8PBw3twaVe7fv48dO3agefPm6NGjB4D/dXdXXZetEh0dzY0ZevnD9VW+vr7Iy8vDqlWreMnlnTt3sGjRIvz8888wMjKCSCTC4cOHsXbtWrn7yKuOT1NT0zq1sybTp0+Hqakp0tLSePPyv8kxfeLECd4XmuzsbGzatAk6Ojq8qXlrooztoqz3JlD5Pnl5P1f1ML06cDAvL4/rvXx5/apv94rGJVSt279/f1y6dAmHDx+GmZmZXJJV1+1X6+sojKSe/fbbb2CMwdvbu9YM6ZNPPsFff/2FqKgoDB8+HADg6ekJAwMDpKamwsnJSW5wmrOzM8aMGYMdO3ZgwIAB8PDwgEgkwsmTJ5GZmQl/f/9qB768TFtbG15eXvjzzz8xbNgw9OjRAy9evEBsbCyePXsGQ0NDFBYWQiaTQU1NDWPHjsWxY8ewefNmXLp0CTY2Nrh58yYuXrwIAwMD3gd/fcRXF/Pnz8dff/2FM2fO4PDhwxg4cCA+/PBDzJo1C8uXL8fAgQPRs2dPGBoaIj4+Hnfv3oWnp6fcB36TJk3g4uKCM2fOQFtbm7vXtoqBgQGWLFmCmTNnwtfXF7169ULz5s2RlJSE69evo0uXLm88z/fw4cMRExODoKAgeHt7QyQS4dixYzAzM5NLPoKDg3HlyhVERkbi1KlTsLGxQVZWFo4fPw51dXUsXbq0TgOwFi1aBENDQ2zduhVeXl7o3r07LCwsIBQKcfv2bSQmJqK8vBzm5uZYuHAh97yqiaj+/PNPpKWlwc3NjRtZnZeXhxkzZuCDDz547W3wJvusOt7e3vjpp58wZMgQ9OrVCxUVFUhISEB6ejpMTEyQk5ODvLw87htw8+bNce/ePYSFhcHDwwM9e/bEokWLMGrUKEybNg3u7u6wsLBAeno64uLiYGRkhLCwMO71QkND4e/vj6CgIPTq1QstWrRAbGzsaw2CAyrvMpg5cyZOnjwJY2NjxMbGIiMjAxMnTqz227+vry8WL15c7ZeFmjg5OSEkJAQrVqyAn58fHB0d0blzZ2hoaODevXuIj4+HSCTCjz/+yJ3wq7pvp06digEDBkBPTw/Xr19HUlISmjRpgufPn9eaqE2cOBEJCQnYsWMHLl26BCcnJxQUFODYsWMoKSnBypUruV7J6dOnY8qUKfD19UXfvn1haGiIGzdu4Pz583BycuKSlDelo6OD0NBQfPnll9iwYQP69euHtm3bvtExrauri7Fjx6Jv377Q09PDyZMn8ezZMyxevLhOU2ErY7so670JVL5P7t+/j5kzZ8LV1RU+Pj6wsbHBxYsXMXLkSDg4OCA3NxcnT56ERCKBtrY2r6eyaurzo0ePQkdHB76+vrUO+PPx8cHOnTtx584dbr6QN91+NVJ4Y2I969OnDxOLxXL3ab6qtLSUOTo6MrFYzFJTU7nyqnvW9+zZU+Nzo6OjmZ+fH7O1tWUODg5syJAhbN++fUwqlXLr1HQPKGOV97NGREQwT09P1qVLF9azZ082ZcoUlpyczCIiIphYLObdY52Xl8fCw8NZjx49mJWVFfP19WVnzpxhQ4cOZXZ2dm8UX01qmofgVTt27GBisZi5uLiwvLw8rjwuLo4FBAQwBwcHZmdnx7y9vdlPP/3E3Yv7qkOHDsndP/uqy5cvsy+++II5OTmxLl26sL59+7I1a9bw7hFm7PXmFqh6bR8fH2Ztbc3c3d3ZqlWr2NOnT6tdNzc3ly1fvpx9/PHHzMrKirm5ubHAwEC5e/Lr4ubNm2zx4sVs4MCBrGvXrtwxMH36dHbixIlq91NFRQXbtm0bGzRoELOxsWFOTk5s5MiR7MSJE3VqK2P/u2f85s2bvPK67LPq7nevUlZWxtatW8e8vLxYly5dmIeHBxs/fjw7c+YM27p1q9wcBbGxsdx2nDt3Llf++J1Q4MwAACAASURBVPFjNn/+fObm5sasrKyYp6cnCw4OZg8fPpR7zX/++YdNnz6dde/endnZ2bGpU6eyv//++7XmIViyZAk7cOAA6927N7OysmIDBgyQm0vhZc+ePWNisZiNGjWq1vqrk5KSwubNm8d69+7N7OzsWJcuXVjv3r3Z4sWLWWZmptz6f/zxBxs8eDCzs7Njzs7ObNiwYWznzp0sJSWFicViNnPmTF5bXt0vJSUlbN26daxfv37M2tqaubi4sPHjx3NzqrwsISGBffrpp+w///kPs7a2Zr1792Zr167lzWVSk5rmIXhV1bwh48aN48re5Jjevn078/DwYLa2tszf3583D8mr61ZHGdtFWe/N5ORkNmDAAGZlZcXGjh3LGKuc/yIkJIS5ubkxGxsb1rt3bzZz5kx29+5dNnnyZCYWi9mDBw+4OjZu3Mi6d+/ObGxs2L59+xhj1c8DUqV3795MLBZz8zq8zfarjoAx+uGAt5GRkQETE5Nqu508PT2hra1d7SxThJD6lZCQgM8++wxLlizBsGHDGjqcf43ExEQEBAQgICAA8+bNa+hwyFto8B83et8tXrwYXbt2lbuWdeTIETx+/LheLgEQQmpXNYe7np5enW/FI4TwNfjPH7/v/Pz8cPr0aQwdOhS9e/eGkZER7t69i7i4OLRs2bLaaz2EkPrx8OFDTJ06FdnZ2cjJycFXX31V7RwIhBDFKCF4Sz179sTWrVvx008/ITY2Fvn5+WjWrBlGjBiByZMn827TI4TUryZNmqCwsBBlZWUYPXo0vvjii4YOiZD3Fo0hIIQQQsj72UOQnS0/CxohhBDSWDVrpq94pbdEgwoJIYQQQgkBIYQQQighIIQQQggoISCEEEIIKCEghBBCCCghIIQQQggoISCEEEIIKCEghBBCCCghIIQQQggoISCEEELe2OXLF7Fw4TxcvnyxoUN5a+/l1MWEEELIuyAqahfS0++htLQEDg6ODR3OW6EeAkIIIeQNlZSU8h7fZ5QQEEIIIYQSAkIIIYRQQkAIIYQQUEJACCGEEFBCQAghhBBQQkAIIYQQUEJACCGEEFBCQAghhBBQQkAIIYQQUEJACCGEEFBCQAghhBBQQkAIIYQQKPnXDr///nvExMSgvLwcI0aMgJOTE0JCQiAQCGBhYYGwsDCoqalh/fr1iIuLg7q6OubOnQsbGxtlhkUIIYSQVyithyAxMRFXrlzB7t27sWPHDjx58gTLli1DUFAQdu3aBcYYTp06hZSUFCQlJSEqKgqrV6/GwoULlRUSIYQQQmqgtB6ChIQEiMViTJkyBUVFRZg9ezb27t0LJycnAIC7uzvOnj2Ldu3awdXVFQKBAKamppBKpcjJyYGJiUmNdRsb60BdXais0Akh5J12/vx57N27F8OHD4ezs3NDh/OvJhQKuMdmzfQbOJq3o7SEIDc3F48fP8Z3332HjIwMTJo0CYwxCASVG09XVxeFhYUoKiqCkZER97yq8toSgtzcF8oKmxBC3nk//rgF6en3UFBQiPbtrRo6nH81qZRxj9nZhUp7HVUkG0pLCIyMjGBubg6RSARzc3NoamriyZMn3PLi4mIYGBhAT08PxcXFvHJ9/fc7yyKEEGUqKSnlPRJSH5Q2hqBr1644c+YMGGPIyspCSUkJXFxckJiYCACIj4+Ho6MjHBwckJCQAJlMhsePH0Mmk9XaO0AIIYSQ+qe0HgJPT09cuHABQ4cOBWMMoaGhaNOmDRYsWIDVq1fD3Nwcffr0gVAohKOjI/z8/CCTyRAaGqqskP5VLl++iN9/Pwhvb184ODg2dDiEEELecUq97XD27NlyZTt37pQrCwwMRGBgoDJD+deJitqF9PR7KC0toYSAEEKIQjQxUSNF1xgJIYS8DqX2EBBCCCHvImMjTahriN66HmXcdlhRLkFuXlm91PU6KCEg7zQaC0EIUQZ1DRHiD4e/dT0lxTncY33UBwDuA8MBUEJACA+NhSCEENWgMQTknUZjIQghRDUoISCEEEIIJQSEEEIIqWUMwZgxY7jfHajO9u3blRIQIYQQQlSvxoSgaqKgvXv3QktLC4MHD4a6ujoOHz6MsjLVj34khBBCiPLUmBBU/UxxZGQk9u/fz5Xb2dnhk08+UX5khBBCCFEZhWMIysrKkJ6ezv2dlpaGiooKpQZFCCGEENVSOA9BSEgIxowZgxYtWoAxhufPn+Obb75RRWz/OsaGIqiLNOulLqXMniUpQ26+pF7qIoQQ8m5RmBC4uroiJiYGt2/fhkAggKWlJdTVaT4jZVAXaeLSis/rpa6y3Czusb7q7Dr7RwCUEBDyJowNtaEuqp9zp3IS/grk5pfUS13k/aTw6MzPz8fKlSvx8OFDfPvtt1iwYAFCQkJgaGioivgIIaRRUBepI3ljXL3UJfn/D25Jfkm91Wk7+aN6qYe8vxQmBAsWLECPHj1w7do16OjooHnz5pg1axY2b96sivjIe8jAUBOaorf/0RBAOd+EyiQSFOTTnTKEEPIyhQlBRkYG/Pz8sHv3bohEInz99dfw8fFRRWzkPaUpEmHsz9Pqpa6sgmzusb7q3DpuLRrih0MIIeRdpvAuA6FQiMLCQm6Sovv370NNjSY4JIQQQhoThT0EgYGBGDNmDDIzMzF58mRcvXoVS5cuVUVshJB3CP0UNSGNm8KEwN3dHdbW1rh27RqkUikWLVoEAwMDVcRGCHmH0E9RE9K4Kez79/Pzg4mJCT766CN8/PHHMDExwZAhQ1QRGyHkHUI/RU1I41ZjD0FAQACSkpIAAB07duTGEAiFQvTs2VM10RFCCCFEJWpMCKp+zXDJkiWYP3++ygIihBBCiOopHEMwa9YsnDhxAsXFxQAAqVSKjIwMTJtWP7eAEUIIIe8rTZGQ9/g+U5gQzJgxA/n5+Xj48CEcHR2RmJgIBweHOlU+ePBg6OtXTibTpk0b+Pn5ISIiAkKhEK6urpg6dSpkMhnCw8ORlpYGkUiEJUuWwMzM7O1aRaCprsZ7JIQQUv8+cjHDuUsZcOnapqFDeWsKE4K0tDQcP34cERERGDJkCIKCghAUFKSw4rKyyolfduzYwZUNGjQI69atQ9u2bTFx4kSkpKTg0aNHkEgk2LNnD65evYrly5dj06ZNb9Gkt9cYbq/q3cEYp9Pz4dHu/Z5iWqChxnskhJB3idjcBGJzk4YOo14oTAiaNGkCgUCAdu3aIS0tDYMHD0Z5ebnCilNTU1FSUoLx48ejoqICgYGBkEgk+OCDDwBU/mjSuXPnkJ2dDTc3NwCAnZ0dbty48ZZNenuN4faqTs100KmZTkOH8dYMbVqg8NYz6Hdq2tChEEJIo6YwIbCwsMDixYsxYsQIzJw5E0+fPgVjTGHFWlpa+OyzzzBs2DDcv38fEyZM4M1foKuri3/++QdFRUXQ09PjyoVCISoqKmr9RUVjYx2oqyvveo1EUsY91tf8+Y2FqreHdht9aLep/9dUZTvOnz+PvXv3Yvjw4XB2dlbZ69Y3ZfyuBHm30H59dzTEvlCYEISFheHq1avo0KEDAgMDce7cOXzzzTcKK27Xrh3MzMy43gV9fX3k5eVxy4uLi2FgYIDS0lJuwCIAyGQyhT+vHBMTK1fWvHlLtG5tBqlUimvXLsotb9myNVq1agOJRIKUlCtyy01NP0CLFq1QWloCZ2dnSCQSiEQiHD9+EgDQtu2HaNq0BV68KEJaWorc883M2sPEpCkKCwtw584tueXm5mIYGhojPz8X9+7dllveoUMnmJu3xgstE+Qamcstb/bsJkQVL1Cs3Qx5hvJjLFpkX4e6tAxFOi2Qb9BWvv1PkyGUlaNAzxSFeqZyy1tlXYYakyFfvy2KdFvILW/9pHKbXrqUjOfPs3nL1NTUYGvbDQBw//4dFBXlobuWNbe8nJXjclkaAMBSwwxGQv6BXsrKkFz2NwCgk6gdDNR0ecuLZSW4IbkLALAWtYeumjZveYGsGLck6QAAW00LaAk0ecvzpIVIK38AAHDQtERsbCzKy6XccmPjJvjwww4AgOTkC5DJZLznN2nSDB98ULlPrlxJlNs2io69hIR4JCcno6SkFAUFRXLLXz72bt26JrdcFceevr4BcnKe4cGDu3LLLS2toKOjh6ZNm8PBwYH3vgCATp1soKWljaysTDx+/FDu+VZW9hCJRMjMzMCTJ4/kltvYOEIoFOLRowd4+vSJ3HJ7++4AgIcP7yk89nJzn/OWa2howNq6cszT3btpKCjI4y3X1NRC5862AIC//76JoqJC3nJtbV107Fh5LKem3kBJSTFvuZ6ePiwsOgMAbt5MRlkZf44GAwMjtG9vCQC4ffsacgxzect1JCI0Lak83jMM8iAT8L9s6Uk0YVJS2dP38KXnuvXyQLm0Ak8zKreXDAwZhvy2AYBhqRYMy7RRIZDhsUG+3HKjEm0YSLRQriaVe18A786x9+xZFv75577c8rc59jQ0hFBjAggEDC/KjVEilZ9wr4lW5XmjuLwJSqV6vGUCMJhoVb5mUXlTlEn55y01gRTGmhkAgEJJc0hk/POWUFAOI83HAIACSQuUy7S4ZbGxsVBX1+Ide25uLnLx1TeFF2aHDRsGR8fKbvOPP/4Y8+fPh1gsVljxvn37sHz5cgBAVlYWSkpKoKOjg4cPH4IxhoSEBDg6OsLBwQHx8fEAgKtXr9apbkLeJxJJ5SW20lL6QSVSP9QEladudeH7P7KdvDsETEH//4QJE/DFF1/AxsYGotf4SVuJRII5c+bg8ePHEAgEmDlzJtTU1LB06VJIpVK4urri66+/5u4yuH37NhhjWLp0Kdq3b19r3dnZhbUuf1tBQZPx5MljtGxpijVrNir1tV7WrJk+Lq34XGWv97q6zv6xTtu+WTP9evtlQmXYOm6t0o+hlzXU8VTfGks7GkqzZvpI3hhXL3XdzPwbsX+fg6eFCzq3sqiXOm0nf6TS90VDa9ZMH/GHwxs6jGq5DwyX2xequISg8JLB9evXMXr0aACAQCAAYwwCgQC3bsl3D71MJBJVe2lh7969vL/V1NSwaNGi14mZEEL+1Tq3sqi3RICQKgoTgvPnz6siDkIIIYQ0IIVjCCQSCb777jsEBwejqKgI69evh0QiUUVshBBCGrHLly9i4cJ5uHxZfjAuUT2FPQSLFi2CiYkJUlJSIBQK8eDBA8ydOxerVq1SRXx1pm+gBS1NjXqpSxm3V5WWlaOwgH4ljqiWoYE2RJoK3+Z1ooz3haSsAvkFJfVSF3n/NIY5XxoThWeKlJQUHDx4EPHx8dDW1saKFSvg7e2tithei5amBkbO/qVe6nr2rHIwx5NnhfVW564Vo1AISgjeJ0b6ImhoaSpeUQFlfJCWl5Yhr1BxT51IUx1L5+2rl9fMeV7EPdZXnXMjhtZLPeT9RD+p/W5RmBAIBAJIJBLu549zc3O5/xPSmGloaeJIwLi3rufFkyzusT7qA4D+238G6pAQEEJIXSlMCAICAjBu3DhkZ2cjIiICJ0+exJQpU1QRGyGEEEJURGFCMHjwYFhbWyMxMRFSqRSbNm1Cx44dVREbIYQQQlRE4V0GFRUVyMjIgK6uLgwMDJCamoro6GhVxEYIIYQQFVHYQzBjxgw8fvwY7du3540dGDx4sFIDI4QQQojqKEwI0tLScPToURpISAghhDRiChOC9u3bIzs7G82bN1dFPIQ0OhpqAkD6/4+EvOcMDbUgEr27c75IJOXIz6fbGN+EwoSgtLQUffv2hVgs5v240fbt25UaWEMSCDV4j4S8jR4GRrhYWABHffmfVyXkfSMSaVT7OzVvIjc3l3usrzpnzJgB0Jwvb0RhQvDFF1+oIo53ip6pA4qfXIduyy4NHQppBMy1tGGupa14RUIIaUAK7zJwcnKCUCjE3bt3YWdnB4FAACcnJ1XE1mA0DdvCxLI/NA3bNnQohBBCiEooTAi2bduGNWvWYOvWrSguLkZoaCi2bNmiitgIIYQQoiIKE4KDBw9iy5Yt0NbWhrGxMfbt24f9+/erIjZCCCGEqIjChEBNTY03mFBTUxNCoVCpQRFCCCFEteo0hiAyMhIlJSU4efIkJk2aBGdnZ1XERgh5h6j//1036u/53TeXL1/EwoXzcPnyxYYOhZB3isKEYPbs2TAzM4OlpSWio6Ph4eGB4OBgVcRGCHmHmLdxgpGBKczbvN+DiqOiduHWrRRERe1q6FD+9dTV1XmPpGEp3Atqamrw9/eHp6cnpFIphEIh7TxC/oWaGpuhqbFZQ4fx1kpKSnmPpOGIxWLcu3cP5ubmDR0KQS0JQVFREebPn48uXbrgs88+w7Bhw6ChoYH8/HysX7+eLhsQQgh5K82bN6dZcN8hNV4yWL58OVq3bo2xY8cCAExMTHDq1Cl89913+Pnnn1UVHyGEEEJUoMaEICkpCbNmzZK7o8DR0RF3795VemCEEEIIUZ0aEwINDf5I4g0bNnD/19PTU15EhBBCCFG5GhMCHR0d3L9/n/u7devWAIB79+5BR0enTpU/f/4cHh4euHv3Lh48eIARI0Zg5MiRCAsLg0wmAwCsX78eQ4cOhb+/P65du/YWTSGEEELIm6oxIRg/fjwmTZqEM2fOoKSkBKWlpfjrr78wdepUTJw4UWHF5eXlCA0NhZaWFgBg2bJlCAoKwq5du8AYw6lTp5CSkoKkpCRERUVh9erVWLhwYf21jBBCCCF1VuNdBv369UNFRQWWLFmChw8fAgDatm2LadOm4aOPPlJYcWRkJPz9/bF582YAQEpKCvejSO7u7jh79izatWsHV1dXCAQCmJqaQiqVIicnByYmJvXQNEIIIYTUVa0TCnh7e8Pb2xv5+fkAAENDwzpVeuDAAZiYmMDNzY1LCBhjEAgEAABdXV0UFhaiqKgIRkZG3POqyhUlBMbGOlBXf/+mT27WTL+hQ3hrjaENQONoR2NoA6D6dgiFAu6xsWzD+tJYtkdjaEdDtKFOMwzVNRGosn//fggEApw7dw63bt1CcHAwcnJyuOXFxcUwMDCAnp4eiouLeeX6+oo3Qm7uC7my9+EAyM4urHV5Y2gDQO1QlcbQBqBu7TA0EEGkqamCaN6MpKwM+QWSWtdpLPuC2qEar7ZBFfEqZcrBX375hfv/mDFjEB4ejpUrVyIxMRHdu3dHfHw8nJ2d8cEHH2DlypX47LPP8OTJE8hkMrpcQAiRI9LUxOo5X9RLXXnPnnKP9VXn9GXfA6g9ISDkXaeyOYiDg4OxYMECrF69Gubm5ujTpw+EQiEcHR3h5+cHmUyG0NBQVYVDCCGEkJcoTAgkEgm2bNmC9PR0hIaGYuvWrZg4cSLvJ5Frs2PHDu7/O3fulFseGBiIwMDA1wiZEEIIIfVN4a8dLlq0CCUlJbh58yaEQiEePnyIuXPnqiI2QgghhKiIwoQgJSUF06dPh7q6OrS1tREZGYnU1FRVxEYIIYQQFVGYEAgEAkgkEu6WwdzcXO7/hBBCCGkcFI4hCAgIwLhx45CdnY2IiAicPHkSU6ZMUUVshBBCCFERhQnB4MGDYW1tjcTEREilUmzatAkdO3ZURWyEEEIIURGFCUF0dDSAylkEASA1NRWpqakYPHiwciMjhBBCiMooTAgSExO5/5eXl+PSpUtwdHSkhIAQ8l4SCtV4j4SQSgoTgmXLlvH+zsvLw9dff620gAghRJnEbUxwLzMP5q2MFK9MyL/Ia89UqKOjg0ePHikjFkIIUboWRrpoYaTb0GEQ8s5RmBCMGTOGu82QMYaMjAy4u7srPTBCCCGEqI7ChODlaYUFAgGMjY3RoUMHpQZFCCGEENVSOKrGyckJxsbGUFOrXDU3NxcXLlxQemCEEEIIUR2FPQSLFi1CTEwM2rZty5UJBAJs375dqYERQgghRHUUJgQJCQk4duwYtLS0VBEPIYQQQhqAwksGbdu2BWNMFbEQQgghpIEo7CEwNDTEgAEDYG9vD5FIxJW/Oj8BIYQQQt5fChMCNzc3uLm5qSIWQgghhDQQhQmBr68vMjIycOfOHbi6uiIzM5M3wJAQQggh7z+FYwiOHDmCSZMmISIiAvn5+fD398dvv/2mitgIIYQQoiIKE4IffvgBu3fvhq6uLpo0aYKDBw9i8+bNqoiNEEIIISqiMCFQU1ODnp4e93fz5s25SYoIIYQQ0jgoHENgYWGBnTt3oqKiArdu3cKuXbvQsWNHVcRGCCGEEBVR+FU/NDQUWVlZ0NTUxNy5c6Gnp4ewsDBVxEYIIYQQFVHYQxAVFYWxY8dixowZqoiHEEIIIQ1AYULw5MkTDBs2DObm5vDx8YGXlxe0tbUVViyVSjF//nykp6dDKBRi2bJlYIwhJCQEAoEAFhYWCAsLg5qaGtavX4+4uDioq6tj7ty5sLGxqZfGEUIIIaRuFCYEwcHBCA4OxsWLF3HkyBFs2LABtra2WLFiRa3Pi42NBQD8+uuvSExM5BKCoKAgdO/eHaGhoTh16hRMTU2RlJSEqKgoZGZmIjAwEPv376+f1hFCCCGkThQmBADAGEN5eTnKy8shEAigoaGh8Dm9evXCRx99BAB4/PgxmjZtiri4ODg5OQEA3N3dcfbsWbRr1w6urq4QCAQwNTWFVCpFTk4OTExM3rxVhBBCCHktChOCJUuW4MSJE+jUqRN8fHwwf/58aGpq1q1ydXUEBwfjxIkT+PbbbxEbGwuBQAAA0NXVRWFhIYqKimBkZMQ9p6q8toTA2FgH6urCOsXwLmnWTL+hQ3hrjaENQONoR2NoA0DteJc0hjYAjaMdDdEGhQmBmZkZDh48+Mbf2CMjIzFz5kwMHz4cZWVlXHlxcTEMDAygp6eH4uJiXrm+fu0bIjf3hVzZ+3AAZGcX1rq8MbQBoHaoSmNoA/DvaUdjaANA7VCVV9ugingV3nbo5+eHvXv3Ijg4GEVFRVi/fj0kEonCiqOjo/H9998DALS1tSEQCGBtbY3ExEQAQHx8PBwdHeHg4ICEhATIZDI8fvwYMpmMLhcQQgghKqawh2DRokUwMTFBSkoKhEIhHj58iLlz52LVqlW1Pq93796YM2cORo0ahYqKCsydOxft27fHggULsHr1apibm6NPnz4QCoVwdHSEn58fZDIZQkND661xhBBCCKkbhQlBSkoKDh48iPj4eGhrayMyMhLe3t4KK9bR0cHatWvlynfu3ClXFhgYiMDAwDqGTAghhJD6pvCSgUAggEQi4QYD5ubmcv8nhBBCSOOgsIcgICAA48aNQ3Z2NiIiInDy5ElMnjxZFbERQgghREUUJgSDBw/mBgNKpVJs2rSJftyIEEIIaWRqTAgYY0hISIChoSFsbGzQoUMHAMDt27fx2WefYcuWLSoLkhBCCCHKVWNCEB4ejvj4eJSWlmLBggXo2bMnIiMjsW/fPvj6+qoyRkIIIYQoWY0JwZkzZ3D48GHk5ORgzpw52Lx5M5o0aYKDBw9yvQWEEEIIaRxqTAj09fWhq6sLXV1d3L17F19++SU+/fRTVcZGCCGEEBWp8bbDl28tbNKkCSUDhBBCSCNWp4SgLr9uSAghhJD3V42XDG7duoVOnToBqLzj4OX/CwQC3Lp1SzUREkIIIUTpakwIUlNTVRkHIYQQQhqQwqmLCSGEENL4UUJACCGEEEoICCGEEFLHhOD333/Hf//7X5SUlCA6OlrZMRFCCCFExRQmBKtWrcLp06dx/PhxSKVS7N+/H8uXL1dFbIQQQghREYUJQUJCAlauXAlNTU3o6enh559/Rnx8vCpiI4QQQoiKKEwI1NQqV6maqEgikXBlhBBCCGkcapyHoErfvn0RFBSE/Px8bN26FYcOHcLAgQNVERshhBBCVERhQjBx4kScOXMGpqamyMzMRGBgIDw9PVURGyGEEEJURGFCcOHCBWhpaaFnz54AKi8dXL9+HWZmZjAwMFB6gIQQQghRPoUJwYYNG3Djxg24uLiAMYakpCS0bt0aRUVFmDZtGl0+IIQQQhoBhQkBYwyHDh2CqakpACArKwtz587Fjh07MGbMGEoICCGEkEZA4e0CT58+5ZIBAGjRogWePn0KPT09MMaUGhwhhBBCVENhD4GDgwNmzJgBb29vyGQy/PHHH7C3t0dcXBx0dHRqfF55eTnmzp2LR48eQSKRYNKkSejQoQNCQkIgEAhgYWGBsLAwqKmpYf369YiLi4O6ujrmzp0LGxubem0kIYQQQmqnMCFYuHAhfv31V+zZswdCoRAuLi7w8/PD2bNnsWLFihqfd+jQIRgZGWHlypXIzc2Fr68vOnbsiKCgIHTv3h2hoaE4deoUTE1NkZSUhKioKO4uhv3799drIwkhhBBSO4UJgbq6OgYOHIiPP/4YjDFIpVJcuHABHh4etT6vb9++6NOnD/e3UChESkoKnJycAADu7u44e/Ys2rVrB1dXVwgEApiamkIqlSInJwcmJiY11m1srAN1dWFd2/jOaNZMv6FDeGuNoQ1A42hHY2gDQO14lzSGNgCNox0N0QaFCcG3336Lbdu2oaKiAsbGxsjKyoK1tTWioqJqfZ6uri4AoKioCF999RWCgoIQGRnJzXioq6uLwsJCFBUVwcjIiPe8wsLCWhOC3NwXcmXvwwGQnV1Y6/LG0AaA2qEqjaENwL+nHY2hDQC1Q1VebYMq4lU4qDA6OhqnT59G//79sX37dmzatAnGxsZ1qjwzMxMBAQEYNGgQvL29eVMeFxcXw8DAAHp6eiguLuaV6+u/2zuKEEIIaWwUJgTNmzeHnp4eLCwskJqaio8++giZmZkKK3727BnGjx+PWbNmYejQoQCAzp07IzExEQAQHx8PR0dHODg4ICEhATKZDI8fP4ZMJqu1d4AQQggh9U/hJQM9PT1ER0fDysoKO3fuRPPmzVFaWqqw4u+++w4FBQXYuHEjNm7cCACYN28elixZgtWrV8Pc3Bx9+vSBUCiEo6Mj/Pz8IJPJEBoaMnHXrgAADvVJREFU+vatIoQQQshrUZgQRERE4I8//sDgwYMRGxuL0NBQBAUFKax4/vz5mD9/vlz5zp075coCAwMRGBhYx5AJIYQQUt8UJgRr1qzBsmXLAAAhISFKD4gQQgghqqdwDMHt27d5g/4IIYQQ0vgo7CFQU1ODp6cn2rVrB01NTa58+/btSg2MEEIIIaqjMCGYNWuWKuIghBBCSANSeMnAyckJQqEQd+/ehZ2dHQQCATfbICGEEEIaB4UJwbZt27BmzRps3boVxcXFCA0NxZYtW1QRGyGEEEJURGFCcPDgQWzZsgXa2towNjbGvn376MeHCCGEkEZGYUKgpqYGkUjE/a2pqQmh8P37YSFCCCGE1EzhoEInJydERkaipKQEJ0+exJ49e+Ds7KyK2AghhBCiIgp7CGbPng0zMzNYWloiOjoaHh4eCA4OVkVshBBCCFERhT0Ey5cvh4+PD/z9/VURDyGEEEIagMKE4IMPPkBERATy8/Ph7e0Nb29vtGnTRhWxEUIIIURFFF4yGD16NHbv3o0tW7ZAJBJhypQpGDlypCpiI4QQQoiKKEwIAKCwsBBnz57F2bNnIZVK0aNHD2XHRQghhBAVUnjJ4Msvv8TNmzfh5eWFadOmwdbWFunp6aqIjRBCCCEqojAhGD58ONzd3QEAx48fxzfffIPr16/jypUrSg+OEEIIIaqh8JKBhYUF1q5dCw8PD8yePRtOTk44deqUKmIjhBBCiIrUmBCcOHECn332GYYPH468vDysWLECzZs3x9SpU2FiYqLKGAkhhBCiZDVeMggMDES/fv3w66+/wszMDAAgEAhUFhghhBBCVKfGhODQoUM4cOAARo4cidatW2PAgAGQSqWqjI0QQgghKlLjJQOxWIyQkBCcPn0aEydORGJiIp49e4aJEyfi9OnTqoyREEIIIUqmcFChuro6evXqhY0bNyI+Ph7Ozs745ptvVBEbIYQQQlSkThMTVTExMcH48eNx6NAhZcVDCCGEkAbwWgnB60pOTsaYMWMAAA8ePMCIESMwcuRIhIWFQSaTAQDWr1+PoUOHwt/fH9euXVNmOIQQQgipgdISgh9++AHz589HWVkZAGDZsmUICgrCrl27wBjDqVOnkJKSgqSkJERFRWH16tVYuHChssIhhBBCSC2UlhB88MEHWLduHfd3SkoKnJycAADu7u7466+/cOnSJbi6ukIgEMDU1BRSqRQ5OTnKCokQQgghNVA4dfGb6tOnDzIyMri/GWPcPAa6urooLCxEUVERjIyMuHWqyhVNfGRsrAN1daFyAleiZs30GzqEt9YY2gA0jnY0hjYA1I53SWNoA9A42tEQbVBaQvAqNbX/dUYUFxfDwMAAenp6KC4u5pXr6yveCLm5L+TK3ocDIDu7sNbljaENALVDVRpDG4B/TzsaQxsAaoeqvNoGVcSr1EGFL+vcuTMSExMBAPHx8XB0dISDgwMS/q+9e4+punzgOP4+YSCESg5pK8CgjXT8UQsiSrxNQmsmcVFUBjObK6d0WWNcEqZRApmV2QRqUQ1NOxNQcyaTpPKK2MJoEWlacVHSxBhKcDjn/P5wnmnprzgOvxz5vDY2zmXffb6H24fnec732bsXm81GW1sbNptNl0UWERExwA0bIcjIyCAnJ4c333yT4OBgpk+fjpubG+Hh4SQlJWGz2cjNzb1RcUREROQyA1oI/P39MZvNAAQFBbF+/fp/PCctLY20tLSBjCEiIiL/4oZNGYiIiMjgpUIgIiIiKgQiIiKiQiAiIiKoEIiIiAgqBCIiIoIKgYiIiKBCICIiIqgQiIiICCoEIiIiggqBiIiIoEIgIiIiqBCIiIgIKgQiIiKCCoGIiIigQiAiIiKoEIiIiAgqBCIiIoIKgYiIiKBCICIiIqgQiIiICCoEIiIiggqBiIiIoEIgIiIiwDCjAwDYbDaWL19OU1MT7u7uvPrqq4wdO9boWCIiIkPGoBghqK6upre3l08//ZSXXnqJgoICoyOJiIgMKYOiEHzzzTdMnDgRgPvvv5/vv//e4EQiIiJDi8lut9uNDvHyyy8TExPD5MmTAZgyZQrV1dUMGzYoZjRERERueoNihMDb25vz5887bttsNpUBERGRG2hQFIIHHniAr7/+GoD6+npCQkIMTiQiIjK0DIopg0vvMvjpp5+w2+2sXLmSe+65x+hYIiIiQ8agKAQiIiJirEExZSAiIiLGUiEQERERFQIREREZJJcuHmyOHDnCG2+8QVlZmdFRnGKxWMjOzqa1tZXe3l4WL17MtGnTjI7VL1arlWXLlnHixAnc3NzIz88nMDDQ6FhO++OPP4iPj6e0tNRlF8w++eSTjBgxAgB/f3/y8/MNTtR/JSUl7N69G4vFwrx585g9e7bRkfqtoqKCyspKAHp6emhsbGTfvn2MHDnS4GT/ncViITMzk9bWVm655Rby8vJc8ueit7eXrKwsmpub8fb2Jjc3l7vvvtvoWE5TIfib999/n23btuHp6Wl0FKdt27YNHx8fVq1aRUdHB3FxcS5XCGpqagDYtGkTtbW15OfnU1RUZHAq51gsFnJzcxk+fLjRUZzW09MD4LIlGaC2tpZvv/2WjRs30t3dTWlpqdGRnBIfH098fDwAK1asICEhwaXKAMBXX31FX18fmzZtYt++fbz99tusXbvW6Fj9Zjab8fLywmw2c/z4cfLy8vjggw+MjuU0TRn8TWBgoEt+Y15uxowZPP/8847bbm5uBqZxTnR0NHl5eQC0tbXh6+trcCLnFRYWMnfuXPz8/IyO4rQff/yR7u5uFi5cSGpqKvX19UZH6re9e/cSEhLCkiVLePbZZ5kyZYrRka5LQ0MDx44dIykpyego/RYUFITVasVms9HV1eWyF6I7duwYkyZNAiA4OJiff/7Z4ETXxzW/CgNo+vTptLS0GB3jutx2220AdHV18dxzz/HCCy8YnMg5w4YNIyMjg127dvHOO+8YHccpFRUVjB49mokTJ/Lee+8ZHcdpw4cP5+mnn2b27Nn88ssvLFq0iJ07d7rUL/KOjg7a2tooLi6mpaWFxYsXs3PnTkwmk9HRnFJSUsKSJUuMjuEULy8vWltbeeyxx+jo6KC4uNjoSE4ZP348NTU1REdHc+TIEdrb27FarS75TxhohOCmdfLkSVJTU4mNjeWJJ54wOo7TCgsLqaqqIicnhwsXLhgdp9/Ky8vZv38/KSkpNDY2kpGRwenTp42O1W9BQUHMmjULk8lEUFAQPj4+LncePj4+REVF4e7uTnBwMB4eHpw9e9boWE7p7Ozk+PHjREZGGh3FKR999BFRUVFUVVWxdetWMjMzHdNSriQhIQFvb29SU1OpqakhNDTUZcsAqBDclM6cOcPChQtJT08nMTHR6DhO2bJlCyUlJQB4enpiMplc8gdtw4YNrF+/nrKyMsaPH09hYSFjxowxOla/bd682bEteXt7O11dXS53HmFhYezZswe73U57ezvd3d34+PgYHcspdXV1PPLII0bHcNrIkSMdC1RHjRpFX18fVqvV4FT919DQQFhYGGVlZURHRxMQEGB0pOviOuN98p8VFxfT2dnJunXrWLduHXBxsaQrLWqLiYkhKyuL5ORk+vr6yM7OxsPDw+hYQ1ZiYiJZWVnMmzcPk8nEypUrXWq6AGDq1KnU1dWRmJiI3W4nNzfXJUsmwIkTJ/D39zc6htMWLFhAdnY28+fPx2Kx8OKLL+Ll5WV0rH4bO3Ysa9asobS0lBEjRvDaa68ZHem66NLFIiIioikDERERUSEQERERVAhEREQEFQIRERFBhUBERETQ2w5FXFJLSwszZsz4x4Ywc+bMITk5+bqOnZmZSUREhON6+TfKvffeS1NT06DJIzLUqBCIuCg/Pz+2bt1qdAwRuUmoEIjchCZMmMC0adP47rvv8PX1JSEhgbKyMk6dOkVBQQERERGkpKQwbtw4Dh8+TE9PD9nZ2URFRV1xnPLycj788ENMJhOhoaHk5OSwY8cODh48yOrVqwFYu3YtHh4eJCcn88orr3D06FGsViuLFi1i5syZWK1WXn/9dQ4dOoTVaiU+Pp4FCxZcM7vdbqegoIAvv/wSPz8/rFYrERERA/lyiQhaQyDisn7//XdiY2Ov+Lg05H7mzBkmTZrEli1b6Onpobq6mk8++YS0tDQ+/vhjxzG6urqorKxk9erVZGZm0tvb63isqamJ4uJiysrK+Oyzz/D09OTdd9/l8ccf58CBA3R1dQGwfft2YmNjKSoqIjQ0lIqKCjZs2EBxcTHNzc2YzWYAKisr2bx5M1988QWHDx++5nlVVVXxww8/sH37dtasWcNvv/02EC+fiPyNRghEXNS/TRlc2pb1rrvuIiwsDIA777yTzs5Ox3PmzJkDXNy1bcyYMVfM4dfV1TF16lRuv/12AJKSksjKyiIjI4PJkyeza9cuAgICCAgI4I477mD//v389ddflJeXA3DhwgWOHj3KgQMHaGxs5ODBg477m5qaCA8Pv2ruQ4cOERMTw6233sro0aMd5yEiA0uFQOQm5e7u7vj8Wtfsv/x+m812xf4ENpvtiufa7Xb6+vqAi7u8FRUV4e/v71jsZ7PZWLVqFaGhocDFUYpRo0ZRXl5Oeno6MTExAJw9e9axRffVmEwmLr+iuqvtmSDiqjRlIDKE7dixA7i4a1tnZychISGOxyIiIti9ezfnzp0DwGw289BDDwEQHh7OqVOnqK2tJTo6GoDIyEg2btwIXJzOmDVrFidPniQyMhKz2YzFYuH8+fPMnz+f+vr6a2Z6+OGH+fzzz+nt7eXPP/9kz549A3LuInIlVW8RF3VpDcHlHnzwQZYtW/afj9Hc3ExcXBwAb7311hUjBuPGjeOZZ54hJSUFi8VCaGgoK1ascDz+6KOPcu7cOcdIxNKlS1m+fLljIWF6ejqBgYHMnTuXX3/9lbi4OPr6+oiPj3cUi6uJjo6moaGBmTNn4uvr+4+3VorIwNBuhyJDVEpKCkuXLv2/f5yvxm63Y7FYeOqpp8jOznZMEYiIa9OUgYj0y+nTp5kwYQL33XefyoDITUQjBCIiIqIRAhEREVEhEBEREVQIREREBBUCERERQYVAREREgP8B6pdOIjOwNP4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(8,4))\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.barplot(data=clean_data, x='group', y='data', ci=68)\n",
    "plt.xlabel('Employee Id')\n",
    "plt.ylabel('Average Revenue Generated')\n",
    "plt.axhline(clean_data['data'].mean(), linestyle=\"--\", color='darkgray')\n",
    "\n",
    "plt.title('Average Revenue Generated by Sales Representative', fontsize=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:\n",
    "The plot above is misleading since it goes off of means it skews the data.  Further testing indicates there are no statistical differences between representatives in terms of revenue generated over this time span.  The subsequent plot reveals a more accurate depiction of what testing demonstrates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average revenue across all sales representatives: $494.72\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = clean_data['data'].mean()\n",
    "\n",
    "sns.catplot(data=clean_data, x='group', y='data', \n",
    "            kind='swarm', height=6, aspect=1.5)\n",
    "formatter = ticker.FormatStrFormatter('$%1.2f')\n",
    "plt.axhline(mu, color='k', linestyle=\"-\", lw=2)\n",
    "plt.xlabel('Employee Id')\n",
    "plt.ylabel('Average Total Revenue in US Dollars')\n",
    "plt.title('Average Total Revenue by Sales Representaitve', fontsize=20)\n",
    "print(f'Average revenue across all sales representatives: ${round(mu,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#datacleaning for additional testing:\n",
    "clean_data['data'] = clean_data['data'].astype(float)\n",
    "clean_data['group'] = clean_data['group'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>168.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>98.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>174.000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>45.900</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>342.720</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2077</td>\n",
       "      <td>390.000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2103</td>\n",
       "      <td>52.350</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2104</td>\n",
       "      <td>386.400</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2105</td>\n",
       "      <td>490.000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2123</td>\n",
       "      <td>232.085</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2117 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         data group\n",
       "0     168.000     5\n",
       "1      98.000     5\n",
       "2     174.000     5\n",
       "17     45.900     5\n",
       "18    342.720     5\n",
       "...       ...   ...\n",
       "2077  390.000     7\n",
       "2103   52.350     7\n",
       "2104  386.400     7\n",
       "2105  490.000     7\n",
       "2123  232.085     7\n",
       "\n",
       "[2117 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The table below illustrates to accept the null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>   <th>p-adj</th>   <th>lower</th>     <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>2</td>    <td>70.6795</td>  <td>0.7896</td> <td>-67.9146</td>  <td>209.2736</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>3</td>    <td>62.1979</td>  <td>0.8328</td> <td>-65.5036</td>  <td>189.8995</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>4</td>    <td>-2.0207</td>    <td>0.9</td>  <td>-121.7839</td> <td>117.7426</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>5</td>    <td>2.7041</td>     <td>0.9</td>  <td>-174.3746</td> <td>179.7827</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>6</td>   <td>-81.4678</td>  <td>0.7616</td> <td>-236.6872</td>  <td>73.7517</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>7</td>    <td>68.5247</td>    <td>0.9</td>  <td>-84.8486</td>  <td>221.8981</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>8</td>   <td>-24.7293</td>    <td>0.9</td>  <td>-160.2376</td>  <td>110.779</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>9</td>   <td>101.7408</td>  <td>0.7013</td> <td>-80.9369</td>  <td>284.4186</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>3</td>    <td>-8.4815</td>    <td>0.9</td>  <td>-149.2052</td> <td>132.2421</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>4</td>   <td>-72.7002</td>   <td>0.725</td> <td>-206.2617</td>  <td>60.8614</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>5</td>   <td>-67.9754</td>    <td>0.9</td>  <td>-254.6631</td> <td>118.7123</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>6</td>   <td>-152.1472</td> <td>0.1034</td> <td>-318.2452</td>  <td>13.9507</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>7</td>    <td>-2.1547</td>    <td>0.9</td>  <td>-166.5288</td> <td>162.2193</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>8</td>   <td>-95.4088</td>  <td>0.5349</td> <td>-243.2531</td>  <td>52.4356</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>9</td>    <td>31.0613</td>    <td>0.9</td>  <td>-160.9455</td> <td>223.0682</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>4</td>   <td>-64.2186</td>  <td>0.7606</td> <td>-186.4399</td>  <td>58.0027</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>5</td>   <td>-59.4939</td>    <td>0.9</td>  <td>-238.2441</td> <td>119.2564</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>6</td>   <td>-143.6657</td> <td>0.1049</td> <td>-300.7895</td>  <td>13.4581</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>7</td>    <td>6.3268</td>     <td>0.9</td>  <td>-148.9735</td> <td>161.6271</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>8</td>   <td>-86.9273</td>  <td>0.5613</td> <td>-224.6128</td>  <td>50.7583</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>9</td>    <td>39.5429</td>    <td>0.9</td>  <td>-144.7557</td> <td>223.8415</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>4</td>      <td>5</td>    <td>4.7247</td>     <td>0.9</td>  <td>-168.4434</td> <td>177.8929</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>4</td>      <td>6</td>   <td>-79.4471</td>  <td>0.7575</td>  <td>-230.19</td>   <td>71.2959</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>4</td>      <td>7</td>    <td>70.5454</td>  <td>0.8574</td> <td>-78.2959</td>  <td>219.3867</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>4</td>      <td>8</td>   <td>-22.7086</td>    <td>0.9</td>  <td>-153.0653</td>  <td>107.648</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>4</td>      <td>9</td>   <td>103.7615</td>  <td>0.6578</td> <td>-75.1282</td>  <td>282.6512</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>5</td>      <td>6</td>   <td>-84.1718</td>    <td>0.9</td>  <td>-283.5134</td> <td>115.1697</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>5</td>      <td>7</td>    <td>65.8207</td>    <td>0.9</td>  <td>-132.0867</td> <td>263.7281</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>5</td>      <td>8</td>   <td>-27.4334</td>    <td>0.9</td>  <td>-211.8418</td>  <td>156.975</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>5</td>      <td>9</td>    <td>99.0368</td>    <td>0.9</td>  <td>-122.3569</td> <td>320.4304</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>6</td>      <td>7</td>   <td>149.9925</td>  <td>0.1837</td> <td>-28.6232</td>  <td>328.6082</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>6</td>      <td>8</td>    <td>56.7384</td>    <td>0.9</td>  <td>-106.7935</td> <td>220.2704</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>6</td>      <td>9</td>   <td>183.2086</td>  <td>0.1211</td> <td>-21.1229</td>  <td>387.5401</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>7</td>      <td>8</td>   <td>-93.2541</td>  <td>0.6646</td> <td>-255.0348</td>  <td>68.5267</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>7</td>      <td>9</td>    <td>33.2161</td>    <td>0.9</td>  <td>-169.7166</td> <td>236.1487</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>8</td>      <td>9</td>   <td>126.4701</td>  <td>0.4952</td> <td>-63.3213</td>  <td>316.2616</td>  <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeys = sms.stats.multicomp.pairwise_tukeyhsd(clean_data['data'], clean_data['group'])\n",
    "tukeys.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effect Size Testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "tukeyrepdf = fn.tukey_df(tukeys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.7896</td>\n",
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       "      <td>-236.6872</td>\n",
       "      <td>73.7517</td>\n",
       "      <td>False</td>\n",
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       "      <td>5</td>\n",
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       "      <td>68.5247</td>\n",
       "      <td>0.9000</td>\n",
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       "      <td>0.7013</td>\n",
       "      <td>-80.9369</td>\n",
       "      <td>284.4186</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-8.4815</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-149.2052</td>\n",
       "      <td>132.2421</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
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       "      <td>-72.7002</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>-206.2617</td>\n",
       "      <td>60.8614</td>\n",
       "      <td>False</td>\n",
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       "      <td>10</td>\n",
       "      <td>2</td>\n",
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       "      <td>-67.9754</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-254.6631</td>\n",
       "      <td>118.7123</td>\n",
       "      <td>False</td>\n",
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       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>-152.1472</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>-318.2452</td>\n",
       "      <td>13.9507</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-2.1547</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-166.5288</td>\n",
       "      <td>162.2193</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-95.4088</td>\n",
       "      <td>0.5349</td>\n",
       "      <td>-243.2531</td>\n",
       "      <td>52.4356</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>31.0613</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-160.9455</td>\n",
       "      <td>223.0682</td>\n",
       "      <td>False</td>\n",
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       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-64.2186</td>\n",
       "      <td>0.7606</td>\n",
       "      <td>-186.4399</td>\n",
       "      <td>58.0027</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>-59.4939</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-238.2441</td>\n",
       "      <td>119.2564</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>-143.6657</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>-300.7895</td>\n",
       "      <td>13.4581</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6.3268</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-148.9735</td>\n",
       "      <td>161.6271</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>-86.9273</td>\n",
       "      <td>0.5613</td>\n",
       "      <td>-224.6128</td>\n",
       "      <td>50.7583</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>39.5429</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-144.7557</td>\n",
       "      <td>223.8415</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7247</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-168.4434</td>\n",
       "      <td>177.8929</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>-79.4471</td>\n",
       "      <td>0.7575</td>\n",
       "      <td>-230.1900</td>\n",
       "      <td>71.2959</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>70.5454</td>\n",
       "      <td>0.8574</td>\n",
       "      <td>-78.2959</td>\n",
       "      <td>219.3867</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-22.7086</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-153.0653</td>\n",
       "      <td>107.6480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>103.7615</td>\n",
       "      <td>0.6578</td>\n",
       "      <td>-75.1282</td>\n",
       "      <td>282.6512</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>-84.1718</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-283.5134</td>\n",
       "      <td>115.1697</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>65.8207</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-132.0867</td>\n",
       "      <td>263.7281</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-27.4334</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-211.8418</td>\n",
       "      <td>156.9750</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>99.0368</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-122.3569</td>\n",
       "      <td>320.4304</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>149.9925</td>\n",
       "      <td>0.1837</td>\n",
       "      <td>-28.6232</td>\n",
       "      <td>328.6082</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>56.7384</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-106.7935</td>\n",
       "      <td>220.2704</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>183.2086</td>\n",
       "      <td>0.1211</td>\n",
       "      <td>-21.1229</td>\n",
       "      <td>387.5401</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>-93.2541</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>-255.0348</td>\n",
       "      <td>68.5267</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>33.2161</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-169.7166</td>\n",
       "      <td>236.1487</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>126.4701</td>\n",
       "      <td>0.4952</td>\n",
       "      <td>-63.3213</td>\n",
       "      <td>316.2616</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group1 group2  meandiff   p-adj     lower     upper  reject\n",
       "0       1      2   70.6795  0.7896  -67.9146  209.2736   False\n",
       "1       1      3   62.1979  0.8328  -65.5036  189.8995   False\n",
       "2       1      4   -2.0207  0.9000 -121.7839  117.7426   False\n",
       "3       1      5    2.7041  0.9000 -174.3746  179.7827   False\n",
       "4       1      6  -81.4678  0.7616 -236.6872   73.7517   False\n",
       "5       1      7   68.5247  0.9000  -84.8486  221.8981   False\n",
       "6       1      8  -24.7293  0.9000 -160.2376  110.7790   False\n",
       "7       1      9  101.7408  0.7013  -80.9369  284.4186   False\n",
       "8       2      3   -8.4815  0.9000 -149.2052  132.2421   False\n",
       "9       2      4  -72.7002  0.7250 -206.2617   60.8614   False\n",
       "10      2      5  -67.9754  0.9000 -254.6631  118.7123   False\n",
       "11      2      6 -152.1472  0.1034 -318.2452   13.9507   False\n",
       "12      2      7   -2.1547  0.9000 -166.5288  162.2193   False\n",
       "13      2      8  -95.4088  0.5349 -243.2531   52.4356   False\n",
       "14      2      9   31.0613  0.9000 -160.9455  223.0682   False\n",
       "15      3      4  -64.2186  0.7606 -186.4399   58.0027   False\n",
       "16      3      5  -59.4939  0.9000 -238.2441  119.2564   False\n",
       "17      3      6 -143.6657  0.1049 -300.7895   13.4581   False\n",
       "18      3      7    6.3268  0.9000 -148.9735  161.6271   False\n",
       "19      3      8  -86.9273  0.5613 -224.6128   50.7583   False\n",
       "20      3      9   39.5429  0.9000 -144.7557  223.8415   False\n",
       "21      4      5    4.7247  0.9000 -168.4434  177.8929   False\n",
       "22      4      6  -79.4471  0.7575 -230.1900   71.2959   False\n",
       "23      4      7   70.5454  0.8574  -78.2959  219.3867   False\n",
       "24      4      8  -22.7086  0.9000 -153.0653  107.6480   False\n",
       "25      4      9  103.7615  0.6578  -75.1282  282.6512   False\n",
       "26      5      6  -84.1718  0.9000 -283.5134  115.1697   False\n",
       "27      5      7   65.8207  0.9000 -132.0867  263.7281   False\n",
       "28      5      8  -27.4334  0.9000 -211.8418  156.9750   False\n",
       "29      5      9   99.0368  0.9000 -122.3569  320.4304   False\n",
       "30      6      7  149.9925  0.1837  -28.6232  328.6082   False\n",
       "31      6      8   56.7384  0.9000 -106.7935  220.2704   False\n",
       "32      6      9  183.2086  0.1211  -21.1229  387.5401   False\n",
       "33      7      8  -93.2541  0.6646 -255.0348   68.5267   False\n",
       "34      7      9   33.2161  0.9000 -169.7166  236.1487   False\n",
       "35      8      9  126.4701  0.4952  -63.3213  316.2616   False"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyrepdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 3 Results Table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>padj</th>\n",
       "      <th>d</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7896</td>\n",
       "      <td>-0.130744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8328</td>\n",
       "      <td>-0.114624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.004144</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>0.9000</td>\n",
       "      <td>-0.005335</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.7616</td>\n",
       "      <td>0.170314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.120978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.052428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7013</td>\n",
       "      <td>-0.176757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.014775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0.143003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.123877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.298368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.003517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5349</td>\n",
       "      <td>0.192116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.049137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7606</td>\n",
       "      <td>0.124941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>0.277284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.010479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5613</td>\n",
       "      <td>0.171933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.063944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.010070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.7575</td>\n",
       "      <td>0.178198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.8574</td>\n",
       "      <td>-0.133375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.051141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.6578</td>\n",
       "      <td>-0.194860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.193965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.110550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.063274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.159411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1837</td>\n",
       "      <td>-0.275056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.140752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1211</td>\n",
       "      <td>-0.329617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0.178140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-0.048048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.4952</td>\n",
       "      <td>-0.239676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   g1 g2    padj         d\n",
       "0   1  2  0.7896 -0.130744\n",
       "1   1  3  0.8328 -0.114624\n",
       "2   1  4  0.9000  0.004144\n",
       "3   1  5  0.9000 -0.005335\n",
       "4   1  6  0.7616  0.170314\n",
       "5   1  7  0.9000 -0.120978\n",
       "6   1  8  0.9000  0.052428\n",
       "7   1  9  0.7013 -0.176757\n",
       "8   2  3  0.9000  0.014775\n",
       "9   2  4  0.7250  0.143003\n",
       "10  2  5  0.9000  0.123877\n",
       "11  2  6  0.1034  0.298368\n",
       "12  2  7  0.9000  0.003517\n",
       "13  2  8  0.5349  0.192116\n",
       "14  2  9  0.9000 -0.049137\n",
       "15  3  4  0.7606  0.124941\n",
       "16  3  5  0.9000  0.108117\n",
       "17  3  6  0.1049  0.277284\n",
       "18  3  7  0.9000 -0.010479\n",
       "19  3  8  0.5613  0.171933\n",
       "20  3  9  0.9000 -0.063944\n",
       "21  4  5  0.9000 -0.010070\n",
       "22  4  6  0.7575  0.178198\n",
       "23  4  7  0.8574 -0.133375\n",
       "24  4  8  0.9000  0.051141\n",
       "25  4  9  0.6578 -0.194860\n",
       "26  5  6  0.9000  0.193965\n",
       "27  5  7  0.9000 -0.110550\n",
       "28  5  8  0.9000  0.063274\n",
       "29  5  9  0.9000 -0.159411\n",
       "30  6  7  0.1837 -0.275056\n",
       "31  6  8  0.9000 -0.140752\n",
       "32  6  9  0.1211 -0.329617\n",
       "33  7  8  0.6646  0.178140\n",
       "34  7  9  0.9000 -0.048048\n",
       "35  8  9  0.4952 -0.239676"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.mult_Cohn_d(tukeyrepdf, reps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Findings and recommendations: \n",
    "\n",
    "\n",
    "#### Findings:\n",
    "Both parametric and non parametric tests were conducted, despite indications for non-parametric tests as the dataset(s) did not meet the assupmtion of equal variance. A visual inspection after outlier removal suggested there could be significant variance in the mean and the parametric was run for comparison.  The parametric test indicated that the null hypothesis could be rejected.  However, post-hoc testing validated the efficacy of the non-parametric test to **accept the null hypothesis** taht all sales representatives generate equal revenue.  The smallest probability that the outcome was due to chance was well over the accepted rate of .05. \n",
    "\n",
    "The Hypothesis 3 Results Table above provides details on how each representative compares with one another and is itemized by the adjusted p values and d is the result of a Cohen D illustrating the effect size of each comparison.\n",
    "\n",
    "\n",
    "#### Recommendation:    \n",
    "If there is no statistical difference, and effect size is small at best, best practices can still be shared by those who have a higher average revenue, examples 2,3 and 9 still have higher than average sales.\n",
    "    \n",
    "Perhaps a little healthy, insentivised competition might spur increased revenues if not by one, by many.  Also, building on knowledge of smaller discounts yielding larger quantities, sales team could increase revenue by being conservative with discount rates.\n",
    "   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPOTHESIS 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  ***Where are the customers from that spend the most money?  Do some regions generate statistically significantly more revenue than others?***\n",
    "\n",
    "$H0$: Customers spend equal amounts regardless of region.\n",
    "\n",
    "$H1$: Region has an effect on total amount spent.\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing and inspecting data from  OrderDetail and Order\n",
    "\n",
    "These table includes information on:\n",
    "\n",
    "    1) Sales total information \n",
    "    2) Regions where orders shipped to indicating the location of customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ShipRegion  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0  Western Europe    10248         11       14.0        12       0.0\n",
       "1  Western Europe    10248         42        9.8        10       0.0\n",
       "2  Western Europe    10248         72       34.8         5       0.0\n",
       "3  Western Europe    10249         14       18.6         9       0.0\n",
       "4  Western Europe    10249         51       42.4        40       0.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT \n",
    "                ShipRegion, \n",
    "                OrderID, \n",
    "                ProductID, \n",
    "                UnitPrice,\n",
    "                Quantity, \n",
    "                Discount\n",
    "                FROM `Order` AS o\n",
    "                JOIN OrderDetail AS od\n",
    "                on o.ID = od.OrderId ;\"\"\")\n",
    "dfreg = pd.DataFrame(cur.fetchall(), columns=[x[0] for x in cur.description])\n",
    "dfreg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfreg['Amount_Spent'] = ((dfreg['UnitPrice'])*(1 - dfreg['Discount']))*dfreg['Quantity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4 Preliminary Visualizations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average total spend is $587.37\n",
      "The distribution indicates there may be outliers.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "spend_mu = dfreg['Amount_Spent'].mean()\n",
    "sns.distplot(dfreg['Amount_Spent'], ax=ax)\n",
    "plt.axvline(spend_mu, color='lightgreen')\n",
    "plt.title('Average Total Spend')\n",
    "print(f'The average total spend is ${round(spend_mu,2)}')\n",
    "print(f'The distribution indicates there may be outliers.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "regs = {}\n",
    "for reg in dfreg['ShipRegion'].unique():\n",
    "    regs[reg] = dfreg.groupby('ShipRegion').get_group(reg)['Amount_Spent']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9 regions, they are ['Western Europe', 'South America', 'Central America', 'North America', 'Northern Europe', 'Scandinavia', 'Southern Europe', 'British Isles', 'Eastern Europe'].\n"
     ]
    }
   ],
   "source": [
    "regions = list(dfreg['ShipRegion'].unique())\n",
    "regions\n",
    "print(f'There are {len(regions)} regions, they are {regions}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributions appear roughly equal, although there appears to be outliers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "for k,v in regs.items():\n",
    "    sns.distplot(v,label=v)\n",
    "\n",
    "\n",
    "plt.title('Sales Revenue Distribution')\n",
    "plt.ylabel('Distribution')\n",
    "print('Distributions appear roughly equal, although there appears to be outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a86c6c3e5a9487d95a43d7464ffb1dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='d', options=('Western Europe', 'South America', 'Central America',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def plt_discounts(d=regions):\n",
    "    sns.distplot(dfreg.groupby('ShipRegion').get_group(d)['Amount_Spent'])\n",
    "    plt.axvline(spend_mu, color='purple')\n",
    "    plt.gca()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "sns.barplot(data=dfreg, x='ShipRegion', y='Amount_Spent', ci=68, palette=\"rocket\", ax=ax)\n",
    "plt.title('Total Spend by Region', fontsize=16)\n",
    "plt.axhline(spend_mu,linestyle=\"--\", color='orange', linewidth=.6 )\n",
    "plt.xlabel('Region')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Total Spend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hypothesis 4 Initial Observations:\n",
    "Datatype is numeric in this 2155 order sample.\n",
    "\n",
    "There are 9 regions in reflected in this dataset\n",
    "\n",
    "The avarage of total spent is $587.37.\n",
    "\n",
    "Initial visual inspection indicates skewed, but roughly uniform distribution in total sales, more than half of the sales representatives achieve the average. Additional testing will demonstrate if it is significant.\n",
    "\n",
    "Since we are comparing multiple regions to inspect it's impact on quantity ordered an AVNOVA test will be run: Assumptions for ANOVA Testing:\n",
    "\n",
    "1) Upon a quick visual inspection, there appears to be some outliers that could be removed\n",
    "\n",
    "2) Equal variance\n",
    "\n",
    "3) Normality (if n>15) Not required for samples greater than 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4 Assumption 1: Outlier \n",
    "Outliers were identified and removed via z-score testing.  Details are below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "regs = {}\n",
    "for reg in dfreg['ShipRegion'].unique():\n",
    "    regs[reg] = dfreg.groupby('ShipRegion').get_group(reg)['Amount_Spent']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11 outliers in the Western Europe\n",
      "Found 2 outliers in the South America\n",
      "Found 1 outliers in the Central America\n",
      "Found 9 outliers in the North America\n",
      "Found 2 outliers in the Northern Europe\n",
      "Found 1 outliers in the Scandinavia\n",
      "Found 3 outliers in the Southern Europe\n",
      "Found 4 outliers in the British Isles\n",
      "Found 0 outliers in the Eastern Europe\n",
      "\n",
      " All of these outliers were removed\n"
     ]
    }
   ],
   "source": [
    "for reg, reg_data in regs.items():\n",
    "    idx_outs = fn.find_outliers_Z(reg_data)\n",
    "    print(f'Found {idx_outs.sum()} outliers in the {reg}')\n",
    "    regs[reg] = reg_data[~idx_outs]\n",
    "print('\\n All of these outliers were removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4 Assumption 2: Equal Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "labels = []\n",
    "for k,v in regs.items():\n",
    "    data.append(v)\n",
    "    labels.append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lavene test for equal variance results are 0.0\n",
      "The groups do NOT have equal variance\n"
     ]
    }
   ],
   "source": [
    "stat,p = stats.levene(*data, center = 'median')\n",
    "print(f'Lavene test for equal variance results are {round(p,4)}')\n",
    "sig = 'do NOT' if p < .05 else 'DO'\n",
    "\n",
    "print(f'The groups {sig} have equal variance')\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4 Assumption 3: Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 734 samples in the data set for Regions #Western Europe.\n",
      "There are 353 samples in the data set for Regions #South America.\n",
      "There are 71 samples in the data set for Regions #Central America.\n",
      "There are 418 samples in the data set for Regions #North America.\n",
      "There are 141 samples in the data set for Regions #Northern Europe.\n",
      "There are 69 samples in the data set for Regions #Scandinavia.\n",
      "There are 134 samples in the data set for Regions #Southern Europe.\n",
      "There are 186 samples in the data set for Regions #British Isles.\n",
      "There are 16 samples in the data set for Regions #Eastern Europe.\n",
      "\n",
      "All samples are >15: Normality Assumption Criterion is met.\n"
     ]
    }
   ],
   "source": [
    "n =[]\n",
    "for reg, samples in regs.items():\n",
    "    print(f'There are {len(samples)} samples in the data set for Regions #{reg}.')\n",
    "    n.append(len(samples)>15)\n",
    "if all(n):\n",
    "    print('\\nAll samples are >15: Normality Assumption Criterion is met.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using non-parametric Kruskal since the data set was not of equal variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal test p value: 0.0\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.kruskal(*data)\n",
    "print(f\"Kruskal test p value: {round(p,6)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>174.0</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>167.4</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1696.0</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1933</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1934</td>\n",
       "      <td>199.5</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1935</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1936</td>\n",
       "      <td>232.5</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2054</td>\n",
       "      <td>591.6</td>\n",
       "      <td>Eastern Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2122 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        data           group\n",
       "0      168.0  Western Europe\n",
       "1       98.0  Western Europe\n",
       "2      174.0  Western Europe\n",
       "3      167.4  Western Europe\n",
       "4     1696.0  Western Europe\n",
       "...      ...             ...\n",
       "1933    54.0  Eastern Europe\n",
       "1934   199.5  Eastern Europe\n",
       "1935   200.0  Eastern Europe\n",
       "1936   232.5  Eastern Europe\n",
       "2054   591.6  Eastern Europe\n",
       "\n",
       "[2122 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cregs = fn.prep_data_for_tukeys(regs)  \n",
    "cregs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500.12534142318566"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = cregs['data'].mean()\n",
    "mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "cregs['data']=cregs['data'].astype(float)\n",
    "cregs['group']=cregs['group'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "      <th>group1</th>          <th>group2</th>      <th>meandiff</th>   <th>p-adj</th>   <th>lower</th>     <th>upper</th>   <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Central America</td> <td>-185.8522</td> <td>0.2271</td> <td>-415.6671</td>  <td>43.9628</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Eastern Europe</td>  <td>-241.5782</td> <td>0.6901</td> <td>-670.7752</td> <td>187.6188</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>   <td>North America</td>  <td>108.0077</td>  <td>0.3368</td> <td>-37.1939</td>  <td>253.2093</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Northern Europe</td>  <td>48.1291</td>    <td>0.9</td>  <td>-135.8232</td> <td>232.0814</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>    <td>Scandinavia</td>   <td>-137.1482</td> <td>0.6379</td> <td>-369.3612</td>  <td>95.0647</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>   <td>South America</td>  <td>-38.1848</td>    <td>0.9</td>  <td>-187.4464</td> <td>111.0767</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Southern Europe</td> <td>-172.9738</td> <td>0.0946</td> <td>-359.6391</td>  <td>13.6914</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Western Europe</td>  <td>124.6055</td>  <td>0.0989</td> <td>-10.6288</td>  <td>259.8398</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Eastern Europe</td>   <td>-55.726</td>    <td>0.9</td>  <td>-511.6242</td> <td>400.1721</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>  <td>North America</td>  <td>293.8599</td>   <td>0.001</td>  <td>82.3968</td>  <td>505.3229</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Northern Europe</td> <td>233.9812</td>  <td>0.0623</td>  <td>-5.7511</td>  <td>473.7136</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>   <td>Scandinavia</td>    <td>48.7039</td>    <td>0.9</td>  <td>-229.7849</td> <td>327.1927</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>  <td>South America</td>  <td>147.6673</td>  <td>0.4491</td> <td>-66.6039</td>  <td>361.9385</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Southern Europe</td>  <td>12.8783</td>    <td>0.9</td>  <td>-228.942</td>  <td>254.6986</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Western Europe</td>  <td>310.4577</td>   <td>0.001</td> <td>105.7104</td>   <td>515.205</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>   <td>North America</td>  <td>349.5859</td>  <td>0.1926</td> <td>-70.0708</td>  <td>769.2426</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Northern Europe</td> <td>289.7073</td>  <td>0.4947</td> <td>-144.8807</td> <td>724.2953</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>    <td>Scandinavia</td>    <td>104.43</td>     <td>0.9</td>  <td>-352.6817</td> <td>561.5417</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>   <td>South America</td>  <td>203.3934</td>  <td>0.8404</td> <td>-217.6853</td>  <td>624.472</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Southern Europe</td>  <td>68.6044</td>    <td>0.9</td>  <td>-367.1389</td> <td>504.3476</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Western Europe</td>  <td>366.1837</td>  <td>0.1372</td> <td>-50.1293</td>  <td>782.4968</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Northern Europe</td> <td>-59.8786</td>    <td>0.9</td>  <td>-220.316</td>  <td>100.5588</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>    <td>Scandinavia</td>   <td>-245.1559</td> <td>0.0115</td> <td>-459.2227</td> <td>-31.0892</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>   <td>South America</td>  <td>-146.1925</td> <td>0.0045</td> <td>-265.2754</td> <td>-27.1097</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Southern Europe</td> <td>-280.9815</td>  <td>0.001</td> <td>-444.5224</td> <td>-117.4406</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Western Europe</td>   <td>16.5978</td>    <td>0.9</td>  <td>-84.3478</td>  <td>117.5435</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td>   <td>Scandinavia</td>   <td>-185.2773</td> <td>0.2974</td> <td>-427.3094</td>  <td>56.7548</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td>  <td>South America</td>  <td>-86.3139</td>  <td>0.7597</td> <td>-250.4349</td>  <td>77.807</td>    <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td> <td>Southern Europe</td> <td>-221.1029</td> <td>0.0164</td> <td>-419.8505</td> <td>-22.3554</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td> <td>Western Europe</td>   <td>76.4764</td>  <td>0.7992</td> <td>-74.9996</td>  <td>227.9525</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>    <td>South America</td>   <td>98.9634</td>  <td>0.8905</td> <td>-117.8778</td> <td>315.8046</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>   <td>Southern Europe</td> <td>-35.8256</td>    <td>0.9</td>  <td>-279.926</td>  <td>208.2748</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>   <td>Western Europe</td>  <td>261.7538</td>   <td>0.003</td>  <td>54.3185</td>   <td>469.189</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>South America</td>  <td>Southern Europe</td> <td>-134.789</td>  <td>0.2306</td> <td>-301.9451</td>  <td>32.3671</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>South America</td>  <td>Western Europe</td>  <td>162.7904</td>   <td>0.001</td>  <td>56.0873</td>  <td>269.4934</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Southern Europe</td> <td>Western Europe</td>  <td>297.5794</td>   <td>0.001</td>  <td>142.82</td>   <td>452.3387</td>   <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyr = sms.stats.multicomp.pairwise_tukeyhsd(cregs['data'],cregs['group'])\n",
    "tukeyr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukeyr.plot_simultaneous();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Noting that the sample size of Eastern Europe is relatively small and the confidence interval is much greater to accomodate for it, possibly skewing the results of the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "tukeyr_df = fn.tukey_df(tukeyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>reject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Central America</td>\n",
       "      <td>-185.8522</td>\n",
       "      <td>0.2271</td>\n",
       "      <td>-415.6671</td>\n",
       "      <td>43.9628</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>-241.5782</td>\n",
       "      <td>0.6901</td>\n",
       "      <td>-670.7752</td>\n",
       "      <td>187.6188</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>North America</td>\n",
       "      <td>108.0077</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>-37.1939</td>\n",
       "      <td>253.2093</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>48.1291</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-135.8232</td>\n",
       "      <td>232.0814</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>-137.1482</td>\n",
       "      <td>0.6379</td>\n",
       "      <td>-369.3612</td>\n",
       "      <td>95.0647</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>South America</td>\n",
       "      <td>-38.1848</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-187.4464</td>\n",
       "      <td>111.0767</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>-172.9738</td>\n",
       "      <td>0.0946</td>\n",
       "      <td>-359.6391</td>\n",
       "      <td>13.6914</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>124.6055</td>\n",
       "      <td>0.0989</td>\n",
       "      <td>-10.6288</td>\n",
       "      <td>259.8398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>-55.7260</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-511.6242</td>\n",
       "      <td>400.1721</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>233.9812</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>-5.7511</td>\n",
       "      <td>473.7136</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>48.7039</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-229.7849</td>\n",
       "      <td>327.1927</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Central America</td>\n",
       "      <td>South America</td>\n",
       "      <td>147.6673</td>\n",
       "      <td>0.4491</td>\n",
       "      <td>-66.6039</td>\n",
       "      <td>361.9385</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>12.8783</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-228.9420</td>\n",
       "      <td>254.6986</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>North America</td>\n",
       "      <td>349.5859</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>-70.0708</td>\n",
       "      <td>769.2426</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>289.7073</td>\n",
       "      <td>0.4947</td>\n",
       "      <td>-144.8807</td>\n",
       "      <td>724.2953</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>104.4300</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-352.6817</td>\n",
       "      <td>561.5417</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>South America</td>\n",
       "      <td>203.3934</td>\n",
       "      <td>0.8404</td>\n",
       "      <td>-217.6853</td>\n",
       "      <td>624.4720</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>68.6044</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-367.1389</td>\n",
       "      <td>504.3476</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>366.1837</td>\n",
       "      <td>0.1372</td>\n",
       "      <td>-50.1293</td>\n",
       "      <td>782.4968</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>North America</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>-59.8786</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-220.3160</td>\n",
       "      <td>100.5588</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>North America</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>16.5978</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-84.3478</td>\n",
       "      <td>117.5435</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>-185.2773</td>\n",
       "      <td>0.2974</td>\n",
       "      <td>-427.3094</td>\n",
       "      <td>56.7548</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>South America</td>\n",
       "      <td>-86.3139</td>\n",
       "      <td>0.7597</td>\n",
       "      <td>-250.4349</td>\n",
       "      <td>77.8070</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>76.4764</td>\n",
       "      <td>0.7992</td>\n",
       "      <td>-74.9996</td>\n",
       "      <td>227.9525</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>South America</td>\n",
       "      <td>98.9634</td>\n",
       "      <td>0.8905</td>\n",
       "      <td>-117.8778</td>\n",
       "      <td>315.8046</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>-35.8256</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>-279.9260</td>\n",
       "      <td>208.2748</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>South America</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>-134.7890</td>\n",
       "      <td>0.2306</td>\n",
       "      <td>-301.9451</td>\n",
       "      <td>32.3671</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             group1           group2  meandiff   p-adj     lower     upper  \\\n",
       "0     British Isles  Central America -185.8522  0.2271 -415.6671   43.9628   \n",
       "1     British Isles   Eastern Europe -241.5782  0.6901 -670.7752  187.6188   \n",
       "2     British Isles    North America  108.0077  0.3368  -37.1939  253.2093   \n",
       "3     British Isles  Northern Europe   48.1291  0.9000 -135.8232  232.0814   \n",
       "4     British Isles      Scandinavia -137.1482  0.6379 -369.3612   95.0647   \n",
       "5     British Isles    South America  -38.1848  0.9000 -187.4464  111.0767   \n",
       "6     British Isles  Southern Europe -172.9738  0.0946 -359.6391   13.6914   \n",
       "7     British Isles   Western Europe  124.6055  0.0989  -10.6288  259.8398   \n",
       "8   Central America   Eastern Europe  -55.7260  0.9000 -511.6242  400.1721   \n",
       "10  Central America  Northern Europe  233.9812  0.0623   -5.7511  473.7136   \n",
       "11  Central America      Scandinavia   48.7039  0.9000 -229.7849  327.1927   \n",
       "12  Central America    South America  147.6673  0.4491  -66.6039  361.9385   \n",
       "13  Central America  Southern Europe   12.8783  0.9000 -228.9420  254.6986   \n",
       "15   Eastern Europe    North America  349.5859  0.1926  -70.0708  769.2426   \n",
       "16   Eastern Europe  Northern Europe  289.7073  0.4947 -144.8807  724.2953   \n",
       "17   Eastern Europe      Scandinavia  104.4300  0.9000 -352.6817  561.5417   \n",
       "18   Eastern Europe    South America  203.3934  0.8404 -217.6853  624.4720   \n",
       "19   Eastern Europe  Southern Europe   68.6044  0.9000 -367.1389  504.3476   \n",
       "20   Eastern Europe   Western Europe  366.1837  0.1372  -50.1293  782.4968   \n",
       "21    North America  Northern Europe  -59.8786  0.9000 -220.3160  100.5588   \n",
       "25    North America   Western Europe   16.5978  0.9000  -84.3478  117.5435   \n",
       "26  Northern Europe      Scandinavia -185.2773  0.2974 -427.3094   56.7548   \n",
       "27  Northern Europe    South America  -86.3139  0.7597 -250.4349   77.8070   \n",
       "29  Northern Europe   Western Europe   76.4764  0.7992  -74.9996  227.9525   \n",
       "30      Scandinavia    South America   98.9634  0.8905 -117.8778  315.8046   \n",
       "31      Scandinavia  Southern Europe  -35.8256  0.9000 -279.9260  208.2748   \n",
       "33    South America  Southern Europe -134.7890  0.2306 -301.9451   32.3671   \n",
       "\n",
       "    reject  \n",
       "0    False  \n",
       "1    False  \n",
       "2    False  \n",
       "3    False  \n",
       "4    False  \n",
       "5    False  \n",
       "6    False  \n",
       "7    False  \n",
       "8    False  \n",
       "10   False  \n",
       "11   False  \n",
       "12   False  \n",
       "13   False  \n",
       "15   False  \n",
       "16   False  \n",
       "17   False  \n",
       "18   False  \n",
       "19   False  \n",
       "20   False  \n",
       "21   False  \n",
       "25   False  \n",
       "26   False  \n",
       "27   False  \n",
       "29   False  \n",
       "30   False  \n",
       "31   False  \n",
       "33   False  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyr_false = tukeyr_df[tukeyr_df['reject']==False]\n",
    "tukeyr_false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>meandiff</th>\n",
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       "      <td>9</td>\n",
       "      <td>Central America</td>\n",
       "      <td>North America</td>\n",
       "      <td>293.8599</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>82.3968</td>\n",
       "      <td>505.3229</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>310.4577</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>105.7104</td>\n",
       "      <td>515.2050</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>North America</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>-245.1559</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>-459.2227</td>\n",
       "      <td>-31.0892</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>North America</td>\n",
       "      <td>South America</td>\n",
       "      <td>-146.1925</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>-265.2754</td>\n",
       "      <td>-27.1097</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>North America</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>-280.9815</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-444.5224</td>\n",
       "      <td>-117.4406</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>-221.1029</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>-419.8505</td>\n",
       "      <td>-22.3554</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>261.7538</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>54.3185</td>\n",
       "      <td>469.1890</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>South America</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>162.7904</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>56.0873</td>\n",
       "      <td>269.4934</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>297.5794</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>142.8200</td>\n",
       "      <td>452.3387</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             group1           group2  meandiff   p-adj     lower     upper  \\\n",
       "9   Central America    North America  293.8599  0.0010   82.3968  505.3229   \n",
       "14  Central America   Western Europe  310.4577  0.0010  105.7104  515.2050   \n",
       "22    North America      Scandinavia -245.1559  0.0115 -459.2227  -31.0892   \n",
       "23    North America    South America -146.1925  0.0045 -265.2754  -27.1097   \n",
       "24    North America  Southern Europe -280.9815  0.0010 -444.5224 -117.4406   \n",
       "28  Northern Europe  Southern Europe -221.1029  0.0164 -419.8505  -22.3554   \n",
       "32      Scandinavia   Western Europe  261.7538  0.0030   54.3185  469.1890   \n",
       "34    South America   Western Europe  162.7904  0.0010   56.0873  269.4934   \n",
       "35  Southern Europe   Western Europe  297.5794  0.0010  142.8200  452.3387   \n",
       "\n",
       "    reject  \n",
       "9     True  \n",
       "14    True  \n",
       "22    True  \n",
       "23    True  \n",
       "24    True  \n",
       "28    True  \n",
       "32    True  \n",
       "34    True  \n",
       "35    True  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyr_trues = tukeyr_df[tukeyr_df['reject']==True]\n",
    "tukeyr_trues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g1</th>\n",
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       "      <th>padj</th>\n",
       "      <th>d</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>Central America</td>\n",
       "      <td>North America</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.485386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Central America</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.548229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>North America</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.402484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>North America</td>\n",
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       "      <td>0.0045</td>\n",
       "      <td>0.262021</td>\n",
       "    </tr>\n",
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       "      <td>4</td>\n",
       "      <td>North America</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.485062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.481990</td>\n",
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       "      <td>6</td>\n",
       "      <td>Scandinavia</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>-0.460358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>South America</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.300535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.539438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                g1               g2    padj         d\n",
       "0  Central America    North America  0.0010 -0.485386\n",
       "1  Central America   Western Europe  0.0010 -0.548229\n",
       "2    North America      Scandinavia  0.0115  0.402484\n",
       "3    North America    South America  0.0045  0.262021\n",
       "4    North America  Southern Europe  0.0010  0.485062\n",
       "5  Northern Europe  Southern Europe  0.0164  0.481990\n",
       "6      Scandinavia   Western Europe  0.0030 -0.460358\n",
       "7    South America   Western Europe  0.0010 -0.300535\n",
       "8  Southern Europe   Western Europe  0.0010 -0.539438"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_Cohn_d(tukeyr_trues, regs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average revenue for Western Europe is $586.93\n",
      "The average revenue for South America is $424.14\n",
      "The average revenue for Central America is $276.47\n",
      "The average revenue for North America is $570.33\n",
      "The average revenue for Northern Europe is $510.45\n",
      "The average revenue for Scandinavia is $325.18\n",
      "The average revenue for Southern Europe is $289.35\n",
      "The average revenue for British Isles is $462.33\n",
      "The average revenue for Eastern Europe is $220.75\n"
     ]
    }
   ],
   "source": [
    "for reg, rev in regs.items():\n",
    "    print(f'The average revenue for {reg} is ${round(rev.mean(),2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4 Observations and Recommendations:\n",
    "\n",
    "#### Findings \n",
    "The average spend for this dataset accross all regions was $500.\n",
    "\n",
    "Regional averages were found to be what is reported in the table below: \n",
    "\n",
    "Region | Average Revenue\n",
    " -- | --|\n",
    "Western Europe | `$586.93`\n",
    "North America | `$570.33`\n",
    "Northern Europe | `$510.45`\n",
    "British Isles | `$462.33`\n",
    "South America | `$424.14`\n",
    "Scandinavia | `$325.18`\n",
    "Southern Europe | `$289.35`\n",
    "Central America | `$276.47`\n",
    "Eastern Europe | `$220.75\n",
    "\n",
    "For each group, the assumption for equal variance was not met and a Kruksal test was conducted.  The p value for the nonparametric kruskal test was singificant, which can reject the null hypothesis that all regions spend the same amounts with a 5% degree of error that this is due to chance.\n",
    "\n",
    "Orders shipped to Western Europe, and North America and Northern Europe generated the highest amount of revenue, statistically these regions are equal:  \n",
    "\n",
    "Region 1 | Region 2 |  MeanDiff | Adj P | Reject Null\n",
    " -- | -- | --| --| --|\n",
    "North America | Northern Europe\t| -59.8786\t| 0.9000 | False\n",
    "North America | Western Europe | 16.5978 | 0.9000\t|\tFalse\n",
    "Northern Europe\t| Western Europe | 76.4764 | 0.7992\t| False\n",
    "\n",
    "\n",
    "For bottom performers that had enough data:\n",
    "\n",
    "Region 1 | Region 2 | Adj P \n",
    " -- | -- | --|\n",
    "South America |\tSouthern Europe\t| 0.2306\t\t\n",
    "\n",
    "#### Recommendations:\n",
    "Explore best practices from regions that are top performers.  \n",
    "Market analysis for regions that need to be developed. \n",
    "Leverage knowledge gained regarding categories and discounts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Western Europe',\n",
       " 'North America',\n",
       " 'Northern Europe',\n",
       " 'British Isles',\n",
       " 'South America',\n",
       " 'Scandinavia',\n",
       " 'Southern Europe',\n",
       " 'Central America',\n",
       " 'Eastern Europe']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexr = list(cregs.groupby('group').mean().sort_values('data', ascending=False).index)\n",
    "indexr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avspend = cregs['data'].mean()\n",
    "avspend\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.barplot(data=cregs, x='group', y='data', ci=68,order=indexr, palette=\"rocket\", ax=ax)\n",
    "plt.title('Average Revenue by Region', fontsize=20)\n",
    "plt.axhline(avspend,linestyle=\"--\", color='orange', linewidth=.6 )\n",
    "plt.xlabel('Region')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Average Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In Closing:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the data provided did not include purchase prices of merchandise, ways were examined to maximize revenues since this is one of the most fundamental business metrics.\n",
    "The datasets were all multi group comparisons and none of the groups met all the assupmtions for parametric testing.  All groups called for Kruskal-Wallis and post-hoc testing detailed in this notebook.\n",
    "\n",
    "It was discovered through hypothesis testing and data analysis that quantity ordered was effected by discounts, and certain products and regions generate higher revenues than others.  It was found that all sales staff generated equal revenue.  \n",
    "\n",
    "Based on the findings, ***recommendations*** were made as possible ways to maximize revneues and are summarized here:\n",
    "\n",
    "\n",
    "> ***Discounts:***\n",
    "\n",
    "* While larger discounts did deomonstrate significant effect on quantity purchased, smaller discounts held a statistically equal effect. To recognize the effect of driving higher quantities purchased and realize larger profit margins, offer the smaller discounts of 5% and 15%.\n",
    " \n",
    "---\n",
    "> ***Product Categories that generate higher revenues:***\n",
    "\n",
    "* Since some product categories generate statistically higher reveneues like meat/poultry, produce and dairy, if there are additional products that align with the higher revenue generating categories, explore ways these categories can be broadened to maximize revenue generating potential.\n",
    "\n",
    "* *Example: Meat/Poultry currently has 6 products, this could be expanded. Conversely, the seafood category carries 12 products which could be narrowed. Additional analysis could demonstrate which seafood are the best sellers which would be preserved.*\n",
    "*  Knowing what revenue each category generates could potentially influence the ability to appropriately categorize discounts. However, not knowing profit margins - we'd need to take this into consideration.\n",
    "---\n",
    "> ***Sales Staff:***\n",
    "\n",
    "* Since  there is no statistical difference between revuenues generated by employees and effect size is small, best practices can still be shared by those who have a higher average revenue, examples employee id 2,3 and 9 have higher than average sales even though not statistically significant.\n",
    "* Perhaps a little healthy, insentivised competition might spur increased revenues if not by one, by many. Also, building on knowledge of smaller discounts yielding larger quantities, sales team could increase revenue by being conservative with discount rates.\n",
    "---\n",
    "> ***Regions that generate higher revenues:***\n",
    "\n",
    "* Since Northern and Western Europe as well as North America generate statistically higher revenues, explore best practices from those regions.\n",
    "* Invest in market analysis for regions that need to be developed. \n",
    "* Leverage knowledge gained regarding categories and discounts.\n",
    "\n",
    "---\n",
    "    \n",
    "    \n",
    "In addition, **future analysis** and testing could provide insight to: \n",
    "  * Develop Regional Markets\n",
    "  * Develop Sales Staff\n",
    "    \n",
    "   "
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    "## Appendix / Reference for hypotheis testing methodologies:\n",
    "\n",
    "**Summary Table - Hypothesis Testing Functions:**\n",
    "\n",
    "Parametric tests (means) | Function | Nonparametric tests (medians) |  Function \n",
    "--| --| --| --|\n",
    "1-sample t test | scipy.stats.ttest_1samp() | 1-sample Wilcoxon | scipy.stats.wilcoxon()\n",
    "2-sample t test | scipy.stats.ttest_ind()   |Mann-Whitney U test| scipy.stats.mannwhitneyu() \n",
    "One-Way ANOVA   | scipy.stats.f_oneway()    |Kruskal-Wallis      | scipy.stats.kruskal()"
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