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        "id": "view-in-github",
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      },
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        "<a href=\"https://colab.research.google.com/github/ziyuliuzilla/Causal-Analysis-with-A-B-Testing/blob/main/Statistical_Inference_of_A_B_Testing_Result.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Causal Effect Analysis"
      ],
      "metadata": {
        "id": "-77wSPNZ5eDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "K7QgP_Y1d5y-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "s3GYys2EoqVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "organic_df = pd.read_excel('/content/Organic.xlsx')\n",
        "non_df = pd.read_excel('/content/Non-organic.xlsx')"
      ],
      "metadata": {
        "id": "DPBNGaYfeNJB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([organic_df, non_df])"
      ],
      "metadata": {
        "id": "R13yGemEe-XC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "non_df.describe()"
      ],
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        "colab": {
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        "id": "KG7VARwNqS7l",
        "outputId": "93e65643-455d-4593-8224-deb90e641a86"
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              "       Organic_Tag  Purchase_Intention  Nurtion_Perception  Item_Preference  \\\n",
              "count         91.0           91.000000           91.000000        91.000000   \n",
              "mean           0.0            2.351648            5.857143         6.835165   \n",
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "       Organic_Tag  Purchase_Intention  Nurtion_Perception  Item_Preference  \\\n",
              "count         95.0           95.000000           95.000000        95.000000   \n",
              "mean           1.0            2.221053            6.663158         7.221053   \n",
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              "std        1.132340                0.980436         0.844188  \n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04ec6581-c3c1-47d1-8aaf-397de45f0865 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04ec6581-c3c1-47d1-8aaf-397de45f0865');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# see distribution\n",
        "distribution1 = sns.displot(non_df,x='Purchase_Intention',bins=[1, 2, 3, 4, 5,6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "g0kveDlsn_JY",
        "outputId": "a16f3284-587d-4fc7-d28b-d12124be9093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# see distribution\n",
        "distribution1 = sns.displot(organic_df,x='Purchase_Intention',bins=[1, 2, 3, 4, 5,6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "VIVJJVMBoBLW",
        "outputId": "7ca31355-47cb-418a-a7e1-b7488ee88898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create Q-Q plot with 45-degree line added to plot\n",
        "fig = sm.qqplot(non_df['Purchase_Intention'], line='45')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "F6bbZQSIosNR",
        "outputId": "f4c36a33-911e-42b3-d3a8-09d6c35204cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create Q-Q plot with 45-degree line added to plot\n",
        "fig = sm.qqplot(organic_df['Purchase_Intention'], line='45')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "0lfV_jEUtDY8",
        "outputId": "79a68c65-19f7-4ab5-b992-76272f75a1be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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9/vth552jjkxSUEVjAFcR7vQPMLMFpXbtBkxNdmAiUgXuIWnbddeFrp8774TevWGHHaKOTFJYRWMALwJvAH2AW0tt3+Du3yY1KhGJ38qVcNVVodunQ4eQz6d166ijkjRQ0RjAd+6+wt0vBFYChYTZQLuaWbPaClBEylFcDAMHhr7+iRPDFM9p01T4S9wqTQVhZj0IKaG/4r9ZQB2o0TRQM2sKDAX2iZ1voLs/VJNzSs2NmruKO8csZn1BYdShUM+g2KFxbg69urSga/vGUYeUOpYvD1M7J08Og7uDBoWZPiJVEE820OuBFu7+TYKvvQW4yd3nmNluwGwzm+DuSxJ8HYnTqLmr6PXqfAqLy80AUqtKwli1voDeIxYCqBLYsgX69w8LtOywQyj4//xnpXGQaolnFtDnwLbrAdSYu3/h7nNi7zcAS4EM/9cdrX75y1Km8N9WQWER/fKXRR1GtBYuhM6doVcvOPlkWLIELrtMhb9UWzwtgE+AyWY2jpASGgB3fzBRQZhZc6A9MKOMfd2B7gDNmmnoIZlWry+IOoQKpXp8SbNpE/zjH+GnQYMw2+f3v1fBLzUWTwvg/4AJwA6EKaAlPwlhZrsCrwHXu/v32+5394HunufueQ2VtCqpGuXmRB1ChVI9vqSYMSMsy3j33XDBBeGu//zzVfhLQsSzHsBdybq4mWUTCv8X3H1Esq4j8enVpUVKjQGUlpOdRa8uLaIOo/b8+GPo5+/fHxo3DlM8Tz896qikjolnFlBD4C+EVcF2Ktnu7ifU5MJmZsAQYGkiu5Ok+koGWDULKGKTJoUZPp98Eub333dfyNsvkmDxjAG8AAwjJIW7EvgTsDYB1/4V8EdgoZnNi237q7uPT8C5pZq6tm+cOQVtqlm/PgzwlqzHO3lyWJ9XJEniqQD2dPchZtYztkbAv81sZk0v7O7vAerIFAEYPTrc7X/1FfzlLyGVQ04GjnlIrYqnAijpC/jCzE4HVgN7JC8kkQyyZk3I3zNsGBx+eFioJS8v6qgkQ8RTAdxrZvWBm4BHgN2BG5IalUhd5x7W4u3ZE374Ae65J+Tuz86OOjLJIPHMAhobe/sdoITiIjX1+edw5ZUwfjx07BiSt7VsGXVUkoHimQX0NGUvCdktKRGJ1FXFxWF1rltugaKiMMWzRw/Iyoo6MslQ8XQBjS31fifgHMI4gIjE66OPQtqGd9+Fk04KWTz33z/qqCTDxdMF9Frpz2b2EvBe0iISqUu2bAlpmu+4A3baCZ56Ci69VE/ySkqIpwWwrYOBvRMdiEidM38+dOsGc+bAOefAgAGw775RRyXys3jGADYQxgAs9volcEuS4xJJX5s2wb33hid499gDXn0Vfvtb3fVLyomnCyhhid9E6rz33w/5+ZcuhUsuCd0/e+4ZdVQiZaqwAjCzHOAPQMkctVnAcHffnOzARNLKDz/A7bfDww9D06bwxhtw6qlRRyVSoXLTQZtZG2AJcAywIvbTBZhqZrlmdm9tBCiS8iZMgDZt4KGH4OqrYdEiFf6SFipqATwMdHf3CaU3mtlJwCJgcTIDE0l569bBTTfB00/DIYfAlClwzDFRRyUSt4oWhNl328IfwN3fJuQHOidpUYmkupEjw9O7Q4dC795hxo8Kf0kzFbUA6pnZju6+qfRGM9sJKHT3jckNTSQFffklXHstDB8O7drBuHFwxBFRRyVSLRW1AIYCr5nZfiUbYmv3vgI8l9ywRFKMe7jbb9kSXn89rM/7wQcq/CWtldsCcPd7zawH8K6Z7Rzb/CNwv7s/UivRiaSCzz6DK66A/Hzo3Dkkbzv00KijEqmxCqeBuvujwKNmtlvs84ZaiUokFRQXw2OPwa23hs+PPBJm+dSrqOEskj7iSgWhgl8yzrJl4YGuqVOhS5eQxXO//Sr/nkga0a2MSGmFhdCnD7RtC0uWwDPPhIe6VPhLHVSdZHAJY2ZPERabX+PureP93qi5q+iXv4zV6wtolJtDry4tKlzIvLLjy9u/7fbjD23IOx+ujetz/ZxszGD9xsKf36/bWEiWGUXu5JaxLd7XkqRMAPUMip2tttVUg52zuePMVpm3OPzcueGuf+5c+N3vQpfPL38ZdVQiSWPuFRcbsQHgm4Bm7n65mR0MtCi1Ulj1L252LPADMDSeCiAvL89vHzSa3iMWUlBY9PP2nOws+pzbpswCa9TcVRUeX97+3x7ZmNdmr9pqeybJzjL6/a5tZlQCP/0Ed98NffvCXnuFfv9zz406KpGEMbPZ7r7dYtPxdAE9DWwCOsU+rwISkgbC3acA31blO/3yl21XKBcUFtEvf1m1ji9v/0szPs/Ywh+gsMjL/ZvWKe+9F+bz9+kTkrctXarCXzJGPBXAge7el/D0L7EHwGotr62ZdTezWWY2a+3a0L1SlupuL29/USUto0xQ3t+mTtiwISzHeMwxIX1zfn5YrKVBg6gjE6k18VQAm2NZQR3AzA4ktAhqhbsPdPc8d89r2LAhjXJzyjyuutvL25+l3O3l/m3SXn4+tG4dunquuw4WLoRTTok6KpFaF08FcAfwJtDUzF4AJgJ/SWpUFejVpQU52Vsvop2TnUWvLi2qdXx5+y88uul22zNJdpaV+zdNW99+C3/6U8jUufPOofvnoYdg112jjkwkEpVWALGEcOcClwIvAXnuPjm5YZWva/vG9Dm3DY1zczCgcW5OuQPA8Rxf3v57u7bZbvvFHZvF/Tk3J5sGO2dv9R7+27Ioa1u8r6XbJvViHxLZXmmwc3bdGwAePhwOOwxefBFuuy3M9OncOeqoRCJV7iwgM6swyYm7z6nxxcMC878B9gK+Au5w9yHlHZ+Xl+ezZs2q6WUlk3zxRejrHzEi5O0ZMiQM+opkkPJmAVX0HMADFexz4ISaBuXuF9b0HCJlcg8Pcd14IxQUhPV5b7oJfhHpoy8iKaWiZHDH12YgIgnz6afQvTu8/XaY5TN4cFiwRUS2UuntUCz//9XArwl3/u8CT7j7T0mOTaRqiopgwICwQEu9emGWzxVXKHmbSDniaQ8PBTYAJSmgLyKsB3BesoISqbKlS0Mah/ffh9NOgyeegGbNoo5KJKXFUwG0dveWpT6/Y2ZLkhWQSJUUFoYUDnffHaZzPvcc/OEPoOc4RCoVT9t4jpl1LPlgZkcDmooj0Zs9G/Ly4PbboWvX0Aq4+GIV/iJxiqcCOBKYZmYrzGwF8D5wlJktNLMFSY1OpCwFBXDLLXD00bB2bVigfdgw2HvvqCMTSSvxdAGdmvQoROI1ZQpcdhl8/HF47dcPcnOjjkokLcXzJPBnwPdAfWDPkh93/yy2TyT5vv8+LMd43HGwZUuY4jlokAp/kRqIZxroPYQ0EP/hv2uOJORBMJG4jB8PV14JK1fCDTfAPffALrtEHZVI2ounC+j3hJTQm5MdjMhWvv46FPjPPw8tW8K0adCxY+XfE5G4xDMIvAhQO1tqjzu88koo9F9+Gf73f2HOHBX+IgkWTwugDzDXzBZRah0Adz8raVFJ5lq9OvT1jx4dpni+/TYcfnjUUYnUSfFUAM8C/wQWAsXJDUcylnvI1HnzzWGFrvvvh549lbxNJIni+de10d0fTnokkrk++QQuvxwmTQqzfAYPhoMOijoqkTovngrgXTPrA4xh6y6gGq8HIBmuqAgefjgs0PKLX8CTT4a5/UreJlIr4qkA2sdeS4/AaRqo1MzixSF524wZcPrpIXlbkyZRRyWSUSqtALQugCTU5s1hcZZ774X69cMSjRdcoPw9IhGIa4TNzE4HWgE7lWxz97uTFZTUUTNnQrdusGgRXHQR9O8PDRtGHZVIxqq0s9XMngDOB64lrD1+HrBfkuOSumTjxjC7p2NHWLcOxoyBF15Q4S8SsXhG2zq7+yXAOne/C+gEaH09ic/kyWEe/wMPhJk+ixfDmWdGHZWIEF8FUBB73WhmjYBCYN9EXNzMTjWzZWa23MxuTcQ5JUV8911YjvH42BDSpElhoLd+/WjjEpGfxVMBjDWzXKAfMAdYAbxY0wubWRYwADgNaAlcaGYtK/6WpIWxY6FVqzCf/+abYcGC/1YEIpIy4pkFdE/s7WtmNhbYyd2/S8C1OwDL3f0TADN7GTgb0HKT6Wrt2vD07ksvQevWMGIEdOgQdVQiUo5yWwBmdpSZ/bLU50uAV4B7zGyPBFy7MfB5qc8rY9u2jaO7mc0ys1lr165NwGUl4dxDod+yJQwfDnfdFZZrVOEvktIq6gJ6EtgMYGbHAvcBQ4HvgIHJDy1w94HunufueQ01ayT1rFwJZ50VpnUeeCDMnRuyd+6wQ9SRiUglKqoAstz929j784GB7v6au/8NSESillVA01Kfm8S2STooLg6pG1q2hIkT4cEHYerU0PcvImmhwgrAzErGCE4EJpXal4gUjTOBg81sfzPbAbiAkG9IUt3y5XDiiWGVrqOOCg923XADZGVFHZmIVEFFBflLwL/N7GvCVNB3AczsIEI3UI24+xYz6wHkA1nAU+6+uKbnlSTasiU8vfu3v4UunkGDQj4fpXEQSUvlVgDu/nczm0iY8/+Wu5esB1yP8FRwjbn7eGB8Is4lSbZwYSjsZ84Mff6PPQaNtxuzF5E0UmFXjrtPL2PbR8kLR1LOpk3wj3+EnwYNYNgwOO883fWL1AFabknKN316uOtfsgQuvjh0/+y5Z9RRiUiCaOUN2d6PP8KNN0LnzvD99zBuHDz3nAp/kTpGLQDZ2sSJIWnbp5/CVVeF3P277x51VCKSBGoBSLB+fSj4TzopLM/473+HgV4V/iJ1lioAgdGjwwNdTz8Nt9wC8+fDscdGHZWIJJm6gDLZmjVw3XVhZk/btvD663DkkVFHJSK1RC2ATOQOzz8Phx0GI0eG9XlnzlThL5Jh1ALINP/3fyGFwxtvQKdOMGRIqAhEJOOoBZApiovh8cdDsrZ//xseegjefVeFv0gGUwsgE3z0EVx2WSjwTzoJBg6E/fePOioRiZhaAHXZli3Qt28Y4F24EJ56Ct56S4W/iABqAdRd8+dDt24wZw6ccw4MGAD77ht1VCKSQtQCqGt++gluvx3y8mDVqrBE44gRKvxFZDtqAdQl06aF5G0ffgh/+lNYpWuPRCzfLCJ1kVoAdcEPP0DPnvDrX8PGjfDmm/DMMyr8RaRCqgDS3YQJ0KYNPPwwXHNNWJ6xS5eooxKRNKAKIF2tWxcGeU85BXbcMUzxfOQR2G23qCMTkTShCiAdjRgRkrcNHQq9e8O8eaH7R0SkCjQInE6+/BJ69IDXXoN27WD8eGjfPuqoRCRNRdICMLPzzGyxmRWbWV4UMaQVd3j22XDXP3ZsWJ/3gw9U+ItIjUTVBbQIOBeYEtH108dnn8Fpp8Gll4YKYN680O2TnR11ZCKS5iKpANx9qbsvi+LaaaO4GB59NCRvmzo1vJ8yBQ49NOrIRKSOSPkxADPrDnQHaNasWcTR1JJly8IDXVOnhimdTz4J++0XdVQiUsckrQVgZm+b2aIyfs6uynncfaC757l7XsOGDZMVbmooLIQ+fULytiVLQr//G2+o8BeRpEhaC8DdT0rWueukuXPDvP558+B3vwtdPvvsE3VUIlKH6TmAqP30UxjUPeqoMM3ztdfg1VdV+ItI0kU1DfQcM1sJdALGmVl+FHFE7r33QnfPfffBJZeEbp9zz406KhHJEFHNAhrp7k3cfUd338fdMyt5zYYN4YGuY46BzZvDIi1PPQUNGkQdmYhkEHUB1bb8fGjdGh57LGTwXLgQTj456qhEJAOpAqgt33wTcvSfeirsskuY4tm/P+y6a9SRiUiGUgWQbO5hVa6WLeHFF8NqXXPnQqdOUUcmIhku5R8ES2tffBFy9I8cCUceGfr627aNOioREUAtgORwh6efDnf9b7wB//wnTJ+uwl9EUopaAIn26afQvTu8/TYceywMGgSHHBJ1VCIi21ELIFGKisKyjK1bw4wZ8Pjj8M47KvxFJGWpBZAIS5bAZZfB+++H1M1PPglNm0YdlYhIhdQCqInCQrj33rAwy0cfwfPPw7hxKvxFJC2oBVBds2eH5G0LFsD554fun733jjoqEZG4qQVQVQUFcMst0KEDrF0Lo0bByy+r8BeRtCw3GHgAAAotSURBVKMWQFVMmRL6+j/+GC6/HPr2hdzcqKMSEakWtQDi8f33cPXVcNxxYbbPxIkwcKAKfxFJa6oAKjN+fFiX98kn4cYbQ5//CSdEHZWISI2pAijP11/DxRfD6afD7rvDtGnwwAMhkZuISB2gCmBb7jBsWEjjMGwY3HEHzJkDRx8ddWQiIgmlQeDSVq+Gq66CMWPCEo0TJ0KbNlFHJSKSFGoBQLjrHzw43PVPmAD33x+e6lXhLyJ1mFoAn3wSpnROmgS/+U1I3nbQQVFHJSKSdFEtCt/PzD40swVmNtLMan8+ZVER/OtfIXnbrFlhls/EiSr8RSRjRNUFNAFo7e6HAx8BvWv16osWQefOYVrniSfC4sUhhXM99YiJSOaIpMRz97fcfUvs43SgSa1cePNmuOsuOOKI0PXz4othwLdJ7VxeRCSVpMIYQDdgWHk7zaw70B2gWbNm1b/KzJkheduiRXDRRWFB9oYNq38+EZE0l7QWgJm9bWaLyvg5u9QxtwFbgBfKO4+7D3T3PHfPa1idAnvjRrj5ZujYEdatg9dfhxdeUOEvIhkvaS0Adz+pov1mdilwBnCiu3tSgpg8OSRv+89/4Iorwtq89esn5VIiIukmqllApwJ/Ac5y940Jv8B334UC//jjw+d33oEnnlDhLyJSSlTTXh4FdgMmmNk8M3siYWd+/fXwQNfgwaHrZ8GCML9fRES2EskgsLsnfrL92rXQsye89FJ4gnfUqJDOQUREypT+E9/dw3TOww6D4cPh7rvDg10q/EVEKpQK00Crb+XKkLxt7NiQrXPIkJC7X0REKpWeLYDi4pC6oWXLkMPnX/+CqVNV+IuIVEH6tQCWLw/J2yZPDmkcBg6EAw6IOioRkbSTXhXAV1+FAd4ddwyzfLp1A7OooxIRSUvpVQGsXAlnnw2PPQaNGkUdjYhIWrNkPYSbDGa2FvgsAafaC/g6AeepLYo3uRRvcine5Ion3v3cfbv8N2lVASSKmc1y97yo44iX4k0uxZtcije5ahJves4CEhGRGlMFICKSoTK1AhgYdQBVpHiTS/Eml+JNrmrHm5FjACIikrktABGRjKcKQEQkQ2VkBWBm/czsQzNbYGYjzSw36pgqYmbnmdliMys2s5SdnmZmp5rZMjNbbma3Rh1PZczsKTNbY2aLoo6lMmbW1MzeMbMlsf8XekYdU0XMbCcz+8DM5sfivSvqmOJhZllmNtfMxkYdSzzMbIWZLYytqzKrqt/PyAoAmAC0dvfDgY+A3hHHU5lFwLnAlKgDKY+ZZQEDgNOAlsCFZtYy2qgq9QxwatRBxGkLcJO7twQ6Atek+N93E3CCu7cF2gGnmlnHiGOKR09gadRBVNHx7t6uOs8CZGQF4O5vufuW2MfpQJMo46mMuy9192VRx1GJDsByd//E3TcDLwNnRxxThdx9CvBt1HHEw92/cPc5sfcbCIVU42ijKp8HP8Q+Zsd+UnrGiZk1AU4HBkcdS23JyApgG92AN6IOog5oDHxe6vNKUriASmdm1hxoD8yINpKKxbpT5gFrgAnuntLxAv0Ja5UXRx1IFTjwlpnNNrPuVf1yeiWDqwIzexv4ZRm7bnP30bFjbiM0rV+ozdjKEk+8Ima2K/AacL27fx91PBVx9yKgXWyMbaSZtXb3lBxvMbMzgDXuPtvMfhN1PFXwa3dfZWZ7E9ZY/zDWso1Lna0A3P2kivab2aXAGcCJngIPQ1QWbxpYBTQt9blJbJskiJllEwr/F9x9RNTxxMvd15vZO4TxlpSsAIBfAWeZ2f8AOwG7m9nz7n5xxHFVyN1XxV7XmNlIQlds3BVARnYBmdmphKbeWe6+Mep46oiZwMFmtr+Z7QBcAIyJOKY6w8wMGAIsdfcHo46nMmbWsGR2nZnlACcDH0YbVfncvbe7N3H35oT/dyeleuFvZruY2W4l74FTqGIFm5EVAPAosBuhyTTPzJ6IOqCKmNk5ZrYS6ASMM7P8qGPaVmxQvQeQTxigfMXdF0cbVcXM7CXgfaCFma00sz9HHVMFfgX8ETgh9v/svNjdaqraF3jHzBYQbg4muHtaTK1MI/sA75nZfOADYJy7v1mVEygVhIhIhsrUFoCISMZTBSAikqFUAYiIZChVACIiGUoVgIhIhlIFILXOzPYsNZXxSzNbFXu/3syW1HIsXUsnVTOzu82syg/lmVnz8rKKmlkrM5sUy5T6HzO7y8wS/m+vot/FzCanciZZiYYqAKl17v5NLHthO+AJ4F+x9+1IQh4WM6voifeuhOylJbH9r7u/ncBr5xAeiLvP3VsAbQhPayYjnXNSfxepe1QBSKrJMrNBsRzyb8UKUMzsQDN7M5b06l0zOzS2vXns7nqBmU00s2ax7c+Y2RNmNgPoW9b3zawzcBbQL9YCOTD2vd/FznGUmU2L5bT/wMx2i13vXTObE/vpXMnvcxEw1d3fAog9ed4D6BW7xp1mdnPJwWa2KJbsDTMbFYt3celEX2b2g5n9PRbXdDPbp7LfpTQzO8XM3o/F/2osvxBmdp+F9QYWmNn9Vf4vJ2lHFYCkmoOBAe7eClgP/Da2fSBwrbsfCdwMPBbb/gjwbGxthxeAh0udqwnQ2d1vLOv77j6NcHfeK9Yi+U/JF2PpLIYBPWM57U8CCgiZLU929yOA87e5XllaAbNLb4hdJ8cqX4ioWyzePOA6M9sztn0XYHosrinA5RX9LqWZ2V7A7cBJsd9hFnBj7NznAK1if8t7K4lN6oA6mwxO0tan7j4v9n420Dx2h9oZeDWkxAFgx9hrJ8JiOQDPAX1LnetVdy+q5PvlaQF84e4zAUoyb8ZyrjxqZu2AIuCQqv+KcbvOzM6JvW9KqBy/ATYDJWkVZhPy7MSrI6GbaGrsb7EDIR3Gd8BPwBALq2EpbUMGUAUgqWZTqfdFQA6hpbo+Nk5QFT/GXqv7/bLcAHwFtI2d96dKjl8CHFt6g5kdAHwTy5K5ha1b4jvFjvkNodXRyd03mtnkkn1AYakMtkVU7d+xEfLyXLjdDrMOwInA7wjdVCdU4byShtQFJCkvdvf9qZmdByEzppm1je2eRsjeCPAH4N0qfn8DITHgtpYB+5rZUbHv7BYbTK5PaBkUE5KzZVUS/gvAr0vNxskhdBvdEdu/Ajgitu8IYP/Y9vrAuljhfyjhzr0y5f0upU0HfmVmB8WuuYuZHRJrJdV39/GESq5tRSeRukEVgKSLPwB/tpD5cDH/XW7yWuD/Wcg6+UfKn11T3vdfBnpZWAj8wJKDY8tang88EvvOBMId+GPAn2LbDuW/rYwyuXsBYXD2NjP7CPiaMChcsgjRa8AeZraYcNf9UWz7m8AvzGwpcB+h4K5Mmb/LNvGsBS4FXor9zd6P/R67AWNj294DbozjepLmlA1UpBaZWVfgQcJC3p9FHY9kNlUAIiIZSl1AIiIZShWAiEiGUgUgIpKhVAGIiGQoVQAiIhlKFYCISIb6/08VZBLJJVS3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Organic to Purchase Intention \n",
        "*   2-sample T-test\n",
        "*   One-way ANOVA\n",
        "\n",
        "*   Mann-Whitney test\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "p_nL11KdpYOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats"
      ],
      "metadata": {
        "id": "jkCficLNtIaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(non_df['Purchase_Intention'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# not follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46UVoriXubGx",
        "outputId": "9197f2ce-08d6-490d-c675-a34250be7a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8695838451385498, pvalue=1.971831693481363e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(organic_df['Purchase_Intention'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# not follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY4KKtMauNOp",
        "outputId": "33bcb2bd-a18e-448d-c0df-cb51df5bd92b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.836064875125885, pvalue=7.1934422862796055e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.levene(non_df['Purchase_Intention'], organic_df['Purchase_Intention'])\n",
        "# equal variance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKzFXS6RvZ0u",
        "outputId": "d1587ea0-4173-4f99-f206-c87a2601ab2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=0.004425453477965599, pvalue=0.9470328126042259)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2-sample t-test\n",
        "res = stats.ttest_ind(non_df['Purchase_Intention'], organic_df['Purchase_Intention'], equal_var=True)\n",
        "\n",
        "display(res)\n",
        "\n",
        "# p-value > 0.05, the difference between the 2 sample means is zero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "0GVU2QzGvlJ9",
        "outputId": "3ac06f36-90e8-431b-c252-cd5ac9a4025e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=0.7229028874505221, pvalue=0.47065743027333196)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# one-way ANOVA\n",
        "from scipy.stats import f_oneway\n",
        "f_oneway(non_df['Purchase_Intention'], organic_df['Purchase_Intention'])\n",
        "\n",
        "# p-value > 0.05, no difference between 2 groups"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLFYTlKNwsZn",
        "outputId": "f8a64af5-48d9-4b47-a801-1db7e63d4499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=0.5225885846843, pvalue=0.47065743027332585)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mann-Whitney test\n",
        "\n",
        "from scipy.stats import mannwhitneyu\n",
        "\n",
        "# Carrying out the Wilcoxon–Mann–Whitney test\n",
        "results = mannwhitneyu(non_df['Purchase_Intention'], organic_df['Purchase_Intention'])\n",
        "results\n",
        "\n",
        "# p-value > 0.05, no difference between 2 groups"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ZfvG-1e0Yus",
        "outputId": "4613a8d8-f332-46b2-dfa0-577f2b023280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MannwhitneyuResult(statistic=4648.5, pvalue=0.3571086327768541)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Organic to Nutrition Perception\n",
        "\n",
        "\n",
        "*   2-sample T-test\n",
        "\n"
      ],
      "metadata": {
        "id": "RwKNV1nCpeqo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(non_df['Nurtion_Perception'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sd6PEQ5TpeCR",
        "outputId": "ea5d984a-8d72-466b-d95e-a7a33dcebba2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9368333220481873, pvalue=0.000258521584328264)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(organic_df['Nurtion_Perception'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lp-NvFuPp3W3",
        "outputId": "8046253e-465a-4e70-fd67-2cd856c2b9aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8897076845169067, pvalue=8.257550234702649e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.levene(non_df['Nurtion_Perception'], organic_df['Nurtion_Perception'])\n",
        "# equal variance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYmbFjAaqAGw",
        "outputId": "1416da21-15da-4fd0-adad-31eafafe7b08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=0.0823315238299806, pvalue=0.774485175908113)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2-sample t-test\n",
        "res2 = stats.ttest_ind(non_df['Nurtion_Perception'], organic_df['Nurtion_Perception'], equal_var=True, alternative='less')\n",
        "\n",
        "display(res2)\n",
        "\n",
        "# p-value < 0.05, organic is higer than non-organic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "i70i6WhZrgJC",
        "outputId": "325673b4-9a9a-4f35-db95-27ac496ec48d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.8585999576033703, pvalue=0.032340513679585584)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Organic to Item Preference\n",
        "\n",
        "\n",
        "*   2-sample T-test\n",
        "\n"
      ],
      "metadata": {
        "id": "_aeRD4tetSCY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(non_df['Item_Preference'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d05ef60-5f84-44f8-a0e5-c6851a51fb38",
        "id": "TrOKw_odtZgx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9318478107452393, pvalue=0.0001366272772429511)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(organic_df['Item_Preference'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8165165d-02b1-493e-a07f-0bb4b2885bb4",
        "id": "gU0OVyTOtZgy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8871015310287476, pvalue=6.37429479866114e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.levene(non_df['Item_Preference'], organic_df['Item_Preference'])\n",
        "# equal variance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08450c0a-42d2-4dd6-e6d0-bb4b97cd1147",
        "id": "4FZQxonBtZgy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=0.32391358914510837, pvalue=0.5699592361849508)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2-sample t-test\n",
        "res3 = stats.ttest_ind(non_df['Item_Preference'], organic_df['Item_Preference'], equal_var=True)\n",
        "\n",
        "display(res3)\n",
        "\n",
        "# p-value > 0.05, no difference"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f47fa43-07b3-4419-d3be-7774eba981f0",
        "id": "xLrG_Xy_tZgz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.0306403773321764, pvalue=0.30406232583858195)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Organic to Health Behavior\n",
        "\n",
        "\n",
        "*   2-sample T-test\n",
        "\n"
      ],
      "metadata": {
        "id": "tcg-laL1uNUg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(non_df['Health_Behavior'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55abed5a-854d-451f-c111-365c86fb60d2",
        "id": "7fBsmw58uSca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8633338809013367, pvalue=1.1397836630067104e-07)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(organic_df['Health_Behavior'])\n",
        "# the null hypothesis is that the data follows a normal distribution\n",
        "# follow normal distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "145d0dae-1a5f-4ebc-f69b-898493d86a62",
        "id": "CQXG0SGsuScb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8160531520843506, pvalue=1.575771069184384e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.levene(non_df['Health_Behavior'], organic_df['Health_Behavior'])\n",
        "# equal variance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8a6c804-4eb9-4962-e10a-a77f192dd46e",
        "id": "ndTCZqNAuScb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=0.0313052946781002, pvalue=0.8597556053731836)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2-sample t-test\n",
        "res3 = stats.ttest_ind(non_df['Health_Behavior'], organic_df['Health_Behavior'], equal_var=True)\n",
        "\n",
        "display(res3)\n",
        "\n",
        "# p-value > 0.05, no difference"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a204f1fc-9e53-4372-8beb-e427cb4b9ba4",
        "id": "oxi5gXMVuScc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=1.4040674611753388, pvalue=0.16198438364797613)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Nurtition Perception + Item Preference to Purchase Intention\n",
        "\n",
        "\n",
        "*   Two-way ANOVA\n"
      ],
      "metadata": {
        "id": "RmRqWGcV-dCG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.formula.api import ols\n",
        "\n",
        "#perform two-way ANOVA\n",
        "model = ols('Purchase_Intention ~ C(Nurtion_Perception) + C(Item_Preference)', data=df).fit()\n",
        "sm.stats.anova_lm(model, typ=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "NyFWgrzz-c1J",
        "outputId": "9b98dd13-a08f-4198-a2a0-d73a66c72fe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           sum_sq     df         F        PR(>F)\n",
              "C(Nurtion_Perception)    9.292968   10.0  1.042848  4.099511e-01\n",
              "C(Item_Preference)      68.707813   10.0  7.710323  4.545456e-10\n",
              "Residual               147.033911  165.0       NaN           NaN"
            ],
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              "      <th></th>\n",
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              "      <th>C(Nurtion_Perception)</th>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Health_Behavior + Item_Preference to Nurtion_Perception\n",
        "\n",
        "\n",
        "*   Two-way ANOVA\n",
        "\n"
      ],
      "metadata": {
        "id": "D3QDwtFaGwYl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.formula.api import ols\n",
        "\n",
        "#perform two-way ANOVA\n",
        "model = ols('Nurtion_Perception ~ C(Health_Behavior) + C(Item_Preference) + C(Health_Behavior):C(Item_Preference)', data=df).fit()\n",
        "sm.stats.anova_lm(model, typ=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "FEQlI12wChb2",
        "outputId": "4f50b39c-a0ca-47a2-b887-5803717ef36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 10, but rank is 3\n",
            "  warnings.warn('covariance of constraints does not have full '\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 30, but rank is 23\n",
            "  warnings.warn('covariance of constraints does not have full '\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            sum_sq     df          F  \\\n",
              "C(Health_Behavior)                        0.830057    3.0   0.050433   \n",
              "C(Item_Preference)                     1861.160334   10.0  33.924690   \n",
              "C(Health_Behavior):C(Item_Preference)   303.025368   30.0   1.841153   \n",
              "Residual                                833.895238  152.0        NaN   \n",
              "\n",
              "                                             PR(>F)  \n",
              "C(Health_Behavior)                     9.849787e-01  \n",
              "C(Item_Preference)                     7.635989e-17  \n",
              "C(Health_Behavior):C(Item_Preference)  1.597298e-02  \n",
              "Residual                                        NaN  "
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Income Level to Environmental Awareness\n",
        "\n",
        "\n",
        "*   Two-way ANOVA\n",
        "\n"
      ],
      "metadata": {
        "id": "30dw4NHdGqZE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#perform two-way ANOVA\n",
        "model2 = ols(' Level_Income ~ C(Enviromental_Awareness)', data=df).fit()\n",
        "sm.stats.anova_lm(model2, typ=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "pmWUYUWpGwMk",
        "outputId": "2bb2d2d1-5387-40b3-d692-83ea4681c56f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               sum_sq     df         F    PR(>F)\n",
              "C(Enviromental_Awareness)   26.868035    4.0  5.975677  0.000154\n",
              "Residual                   203.454545  181.0       NaN       NaN"
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              "      <th>F</th>\n",
              "      <th>PR(&gt;F)</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C(Enviromental_Awareness)</th>\n",
              "      <td>26.868035</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.975677</td>\n",
              "      <td>0.000154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>203.454545</td>\n",
              "      <td>181.0</td>\n",
              "      <td>NaN</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a398baff-3be3-4ba8-9b28-a6ce2f3104cf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a398baff-3be3-4ba8-9b28-a6ce2f3104cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA and Building Model"
      ],
      "metadata": {
        "id": "k_-yVimh5kla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "sC2LKNDu9LlL",
        "outputId": "fa7c5ccf-614c-4551-e851-a1befe589635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Organic_Tag  Purchase_Intention  Nurtion_Perception  Item_Preference  \\\n",
              "0             1                   3                   9                8   \n",
              "1             1                   5                   8                8   \n",
              "2             1                   2                   7                5   \n",
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              "90            0                   3                   5                5   \n",
              "\n",
              "    Level_Income  Enviromental_Awareness  Health_Behavior  \n",
              "0              1                       2                1  \n",
              "1              3                       4                1  \n",
              "2              2                       2                3  \n",
              "3              1                       2                1  \n",
              "4              2                       3                1  \n",
              "..           ...                     ...              ...  \n",
              "86             3                       2                2  \n",
              "87             3                       3                2  \n",
              "88             2                       4                1  \n",
              "89             4                       4                3  \n",
              "90             5                       4                3  \n",
              "\n",
              "[186 rows x 7 columns]"
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5659a551-098b-4458-9173-188f2b2cb408')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5659a551-098b-4458-9173-188f2b2cb408 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5659a551-098b-4458-9173-188f2b2cb408');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yaq6WZQhrBtM",
        "outputId": "32194ebf-f019-4d9c-8f19-530f05ec6dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Organic_Tag  Purchase_Intention  Nurtion_Perception  Item_Preference  \\\n",
              "count   186.000000          186.000000          186.000000       186.000000   \n",
              "mean      0.510753            2.284946            6.268817         7.032258   \n",
              "std       0.501234            1.230025            2.976084         2.553012   \n",
              "min       0.000000            1.000000            0.000000         0.000000   \n",
              "25%       0.000000            1.000000            4.000000         5.250000   \n",
              "50%       1.000000            2.000000            7.000000         7.000000   \n",
              "75%       1.000000            3.000000            9.000000         9.000000   \n",
              "max       1.000000            5.000000           10.000000        10.000000   \n",
              "\n",
              "       Level_Income  Enviromental_Awareness  Health_Behavior  \n",
              "count    186.000000              186.000000       186.000000  \n",
              "mean       2.290323                2.709677         2.096774  \n",
              "std        1.115790                0.942460         0.858213  \n",
              "min        1.000000                1.000000         1.000000  \n",
              "25%        1.000000                2.000000         1.000000  \n",
              "50%        2.000000                2.000000         2.000000  \n",
              "75%        3.000000                3.000000         3.000000  \n",
              "max        5.000000                5.000000         4.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9982d6ca-f442-4663-a4a0-6ce71f194f93\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Organic_Tag</th>\n",
              "      <th>Purchase_Intention</th>\n",
              "      <th>Nurtion_Perception</th>\n",
              "      <th>Item_Preference</th>\n",
              "      <th>Level_Income</th>\n",
              "      <th>Enviromental_Awareness</th>\n",
              "      <th>Health_Behavior</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>186.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.510753</td>\n",
              "      <td>2.284946</td>\n",
              "      <td>6.268817</td>\n",
              "      <td>7.032258</td>\n",
              "      <td>2.290323</td>\n",
              "      <td>2.709677</td>\n",
              "      <td>2.096774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.501234</td>\n",
              "      <td>1.230025</td>\n",
              "      <td>2.976084</td>\n",
              "      <td>2.553012</td>\n",
              "      <td>1.115790</td>\n",
              "      <td>0.942460</td>\n",
              "      <td>0.858213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9982d6ca-f442-4663-a4a0-6ce71f194f93')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9982d6ca-f442-4663-a4a0-6ce71f194f93 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9982d6ca-f442-4663-a4a0-6ce71f194f93');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omFPP7G25jx9",
        "outputId": "471ae925-320c-454c-dcdd-d4acb791c376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "  Using cached https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "Requirement already satisfied: scipy<1.10,>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (1.7.3)\n",
            "Requirement already satisfied: pandas!=1.4.0,<1.6,>1.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (1.3.5)\n",
            "Requirement already satisfied: matplotlib<3.7,>=3.2 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (3.2.2)\n",
            "Requirement already satisfied: pydantic<1.11,>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (1.10.2)\n",
            "Requirement already satisfied: PyYAML<6.1,>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (6.0)\n",
            "Requirement already satisfied: jinja2<3.2,>=2.11.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (2.11.3)\n",
            "Requirement already satisfied: visions[type_image_path]==0.7.5 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (0.7.5)\n",
            "Requirement already satisfied: numpy<1.24,>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (1.21.6)\n",
            "Requirement already satisfied: htmlmin==0.1.12 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (0.1.12)\n",
            "Requirement already satisfied: phik<0.13,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (0.12.2)\n",
            "Requirement already satisfied: requests<2.29,>=2.24.0 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (2.28.1)\n",
            "Requirement already satisfied: tqdm<4.65,>=4.48.2 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (4.64.1)\n",
            "Requirement already satisfied: seaborn<0.13,>=0.10.1 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (0.11.2)\n",
            "Requirement already satisfied: multimethod<1.10,>=1.4 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (1.9)\n",
            "Requirement already satisfied: statsmodels<0.14,>=0.13.2 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (0.13.5)\n",
            "Requirement already satisfied: typeguard<2.14,>=2.13.2 in /usr/local/lib/python3.8/dist-packages (from pandas-profiling===dev) (2.13.3)\n",
            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.8/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling===dev) (2.6.3)\n",
            "Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in /usr/local/lib/python3.8/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling===dev) (0.2.0)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.8/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling===dev) (22.1.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.8/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling===dev) (7.1.2)\n",
            "Requirement already satisfied: imagehash in /usr/local/lib/python3.8/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling===dev) (4.3.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2<3.2,>=2.11.1->pandas-profiling===dev) (2.0.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib<3.7,>=3.2->pandas-profiling===dev) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib<3.7,>=3.2->pandas-profiling===dev) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib<3.7,>=3.2->pandas-profiling===dev) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib<3.7,>=3.2->pandas-profiling===dev) (0.11.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas!=1.4.0,<1.6,>1.1->pandas-profiling===dev) (2022.6)\n",
            "Requirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.8/dist-packages (from phik<0.13,>=0.11.1->pandas-profiling===dev) (1.2.0)\n",
            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.8/dist-packages (from pydantic<1.11,>=1.8.1->pandas-profiling===dev) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib<3.7,>=3.2->pandas-profiling===dev) (1.15.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<2.29,>=2.24.0->pandas-profiling===dev) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.8/dist-packages (from requests<2.29,>=2.24.0->pandas-profiling===dev) (2.1.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<2.29,>=2.24.0->pandas-profiling===dev) (2022.9.24)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<2.29,>=2.24.0->pandas-profiling===dev) (2.10)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.8/dist-packages (from statsmodels<0.14,>=0.13.2->pandas-profiling===dev) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.8/dist-packages (from statsmodels<0.14,>=0.13.2->pandas-profiling===dev) (21.3)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.8/dist-packages (from imagehash->visions[type_image_path]==0.7.5->pandas-profiling===dev) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('df.csv')"
      ],
      "metadata": {
        "id": "yU7eSQyW7eJm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from pandas_profiling import ProfileReport\n",
        "\n",
        "#EDA using pandas-profiling\n",
        "# profile = ProfileReport(pd.read_csv('/content/df.csv'), explorative=True)\n",
        "\n",
        "#Saving results to a HTML file\n",
        "# profile.to_file(\"output.html\")"
      ],
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          "height": 145,
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            "2a319b31b35b413cb6c1ff8182420133",
            "5bacc1438f4a4bea98268ee2257cfcd5",
            "56015d1c33074742916c90298134df41"
          ]
        },
        "id": "0Shrof-K7DWX",
        "outputId": "442f7a25-8304-47ab-9c14-6e8042d249a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26e27b5f8fe54131ba8b5b99e272f52d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b25e34de8ce4290a941163e0fdc938f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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    {
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      "source": [
        "df.corr()"
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        "outputId": "e9cc71a4-2431-4ffb-eb49-54cf4c4aedf0",
        "colab": {
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      "execution_count": null,
      "outputs": [
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              "                        Organic_Tag  Purchase_Intention  Nurtion_Perception  \\\n",
              "Organic_Tag                1.000000           -0.053218            0.135749   \n",
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              "                        Health_Behavior  \n",
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      "execution_count": null,
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              "                        Organic_Tag  Purchase_Intention  Nurtion_Perception  \\\n",
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              "      <td>-0.086770</td>\n",
              "      <td>0.073385</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b08cccd5-227a-43a2-b1fd-ef4ad156e9d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "organic_df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "RIeKyikNwZ5Y",
        "outputId": "4ba72e3b-d98a-4ea9-a8e7-45e60855b00e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Organic_Tag  Purchase_Intention  Nurtion_Perception  \\\n",
              "Organic_Tag                     NaN                 NaN                 NaN   \n",
              "Purchase_Intention              NaN            1.000000           -0.429845   \n",
              "Nurtion_Perception              NaN           -0.429845            1.000000   \n",
              "Item_Preference                 NaN           -0.625368            0.579027   \n",
              "Level_Income                    NaN            0.010303           -0.040739   \n",
              "Enviromental_Awareness          NaN            0.031671            0.120144   \n",
              "Health_Behavior                 NaN            0.028065           -0.227413   \n",
              "\n",
              "                        Item_Preference  Level_Income  Enviromental_Awareness  \\\n",
              "Organic_Tag                         NaN           NaN                     NaN   \n",
              "Purchase_Intention            -0.625368      0.010303                0.031671   \n",
              "Nurtion_Perception             0.579027     -0.040739                0.120144   \n",
              "Item_Preference                1.000000      0.047822                0.130730   \n",
              "Level_Income                   0.047822      1.000000                0.178536   \n",
              "Enviromental_Awareness         0.130730      0.178536                1.000000   \n",
              "Health_Behavior               -0.236178      0.029873               -0.022865   \n",
              "\n",
              "                        Health_Behavior  \n",
              "Organic_Tag                         NaN  \n",
              "Purchase_Intention             0.028065  \n",
              "Nurtion_Perception            -0.227413  \n",
              "Item_Preference               -0.236178  \n",
              "Level_Income                   0.029873  \n",
              "Enviromental_Awareness        -0.022865  \n",
              "Health_Behavior                1.000000  "
            ],
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              "\n",
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              "      <th>Organic_Tag</th>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ab5ee86-3d24-416d-9e03-e094f1538ce7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats.stats import pearsonr\n",
        "\n",
        "#calculation correlation coefficient and p-value between x and y\n",
        "pearsonr(df['Enviromental_Awareness'], df['Level_Income'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLqIO_9wG6n9",
        "outputId": "af92205c-cb03-42cf-83ec-029f6e9aec45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.26563416727411143, 0.0002479198844560381)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pearsonr(df['Organic_Tag'], df['Health_Behavior'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qmWEMhpHdyV",
        "outputId": "3fcda231-0aaa-43b2-d604-9bc70242ce7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.10295913571363842, 0.16198438364798368)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ordered logit model"
      ],
      "metadata": {
        "id": "j190uNpZwNmG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install --upgrade --no-deps statsmodels "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HBLRV70ypnO",
        "outputId": "93e4e3ab-5937-4b87-acbe-8b920126982f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.8/dist-packages (0.13.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.miscmodels.ordinal_model import OrderedModel"
      ],
      "metadata": {
        "id": "krgwSLhH0Mww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod_prob = OrderedModel(df['Purchase_Intention'],\n",
        "                        df[['Organic_Tag', 'Nurtion_Perception', 'Item_Preference','Level_Income','Enviromental_Awareness','Health_Behavior']],\n",
        "                        distr='logit')\n",
        " \n",
        "res_log = mod_prob.fit(method='bfgs')\n",
        "res_log.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "20o2qcAVxTTP",
        "outputId": "e87f6252-0c8a-4aa5-b48e-baf754a08326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 1.156187\n",
            "         Iterations: 33\n",
            "         Function evaluations: 35\n",
            "         Gradient evaluations: 35\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             OrderedModel Results                             \n",
              "==============================================================================\n",
              "Dep. Variable:     Purchase_Intention   Log-Likelihood:                -215.05\n",
              "Model:                   OrderedModel   AIC:                             450.1\n",
              "Method:            Maximum Likelihood   BIC:                             482.4\n",
              "Date:                Sun, 04 Dec 2022                                         \n",
              "Time:                        11:07:08                                         \n",
              "No. Observations:                 186                                         \n",
              "Df Residuals:                     176                                         \n",
              "Df Model:                          10                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "Organic_Tag               -0.0723      0.285     -0.253      0.800      -0.631       0.487\n",
              "Nurtion_Perception        -0.0981      0.060     -1.628      0.104      -0.216       0.020\n",
              "Item_Preference           -0.6512      0.085     -7.624      0.000      -0.819      -0.484\n",
              "Level_Income               0.0571      0.131      0.436      0.663      -0.200       0.314\n",
              "Enviromental_Awareness     0.1942      0.165      1.174      0.240      -0.130       0.518\n",
              "Health_Behavior           -0.4110      0.175     -2.348      0.019      -0.754      -0.068\n",
              "1/2                       -6.5936      0.922     -7.152      0.000      -8.400      -4.787\n",
              "2/3                        0.7142      0.121      5.926      0.000       0.478       0.950\n",
              "3/4                        0.4806      0.155      3.109      0.002       0.178       0.783\n",
              "4/5                        0.2354      0.222      1.058      0.290      -0.201       0.671\n",
              "==========================================================================================\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OrderedModel Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>    <td>Purchase_Intention</td> <th>  Log-Likelihood:    </th> <td> -215.05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>               <td>OrderedModel</td>    <th>  AIC:               </th> <td>   450.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>           <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   482.4</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>              <td>Sun, 04 Dec 2022</td>  <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                  <td>11:07:08</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>       <td>   186</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>           <td>   176</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>               <td>    10</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Organic_Tag</th>            <td>   -0.0723</td> <td>    0.285</td> <td>   -0.253</td> <td> 0.800</td> <td>   -0.631</td> <td>    0.487</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Nurtion_Perception</th>     <td>   -0.0981</td> <td>    0.060</td> <td>   -1.628</td> <td> 0.104</td> <td>   -0.216</td> <td>    0.020</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Item_Preference</th>        <td>   -0.6512</td> <td>    0.085</td> <td>   -7.624</td> <td> 0.000</td> <td>   -0.819</td> <td>   -0.484</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Level_Income</th>           <td>    0.0571</td> <td>    0.131</td> <td>    0.436</td> <td> 0.663</td> <td>   -0.200</td> <td>    0.314</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Enviromental_Awareness</th> <td>    0.1942</td> <td>    0.165</td> <td>    1.174</td> <td> 0.240</td> <td>   -0.130</td> <td>    0.518</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Health_Behavior</th>        <td>   -0.4110</td> <td>    0.175</td> <td>   -2.348</td> <td> 0.019</td> <td>   -0.754</td> <td>   -0.068</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>1/2</th>                    <td>   -6.5936</td> <td>    0.922</td> <td>   -7.152</td> <td> 0.000</td> <td>   -8.400</td> <td>   -4.787</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>2/3</th>                    <td>    0.7142</td> <td>    0.121</td> <td>    5.926</td> <td> 0.000</td> <td>    0.478</td> <td>    0.950</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>3/4</th>                    <td>    0.4806</td> <td>    0.155</td> <td>    3.109</td> <td> 0.002</td> <td>    0.178</td> <td>    0.783</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>4/5</th>                    <td>    0.2354</td> <td>    0.222</td> <td>    1.058</td> <td> 0.290</td> <td>   -0.201</td> <td>    0.671</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mod_prob = OrderedModel(df['Purchase_Intention'],\n",
        "                        df[['Item_Preference','Health_Behavior']],\n",
        "                        distr='logit')\n",
        " \n",
        "res_log = mod_prob.fit(method='bfgs')\n",
        "res_log.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "_aBXSJnK2Ylx",
        "outputId": "52931c34-00a8-4064-b01e-1de60cd1cc9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 1.169084\n",
            "         Iterations: 23\n",
            "         Function evaluations: 25\n",
            "         Gradient evaluations: 25\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             OrderedModel Results                             \n",
              "==============================================================================\n",
              "Dep. Variable:     Purchase_Intention   Log-Likelihood:                -217.45\n",
              "Model:                   OrderedModel   AIC:                             446.9\n",
              "Method:            Maximum Likelihood   BIC:                             466.3\n",
              "Date:                Sun, 04 Dec 2022                                         \n",
              "Time:                        11:17:41                                         \n",
              "No. Observations:                 186                                         \n",
              "Df Residuals:                     180                                         \n",
              "Df Model:                           6                                         \n",
              "===================================================================================\n",
              "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "Item_Preference    -0.7072      0.078     -9.051      0.000      -0.860      -0.554\n",
              "Health_Behavior    -0.4027      0.173     -2.332      0.020      -0.741      -0.064\n",
              "1/2                -6.9326      0.809     -8.569      0.000      -8.518      -5.347\n",
              "2/3                 0.6917      0.120      5.748      0.000       0.456       0.928\n",
              "3/4                 0.4566      0.155      2.953      0.003       0.154       0.760\n",
              "4/5                 0.2288      0.222      1.029      0.303      -0.207       0.665\n",
              "===================================================================================\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OrderedModel Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>    <td>Purchase_Intention</td> <th>  Log-Likelihood:    </th> <td> -217.45</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>               <td>OrderedModel</td>    <th>  AIC:               </th> <td>   446.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>           <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   466.3</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>              <td>Sun, 04 Dec 2022</td>  <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                  <td>11:17:41</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>       <td>   186</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>           <td>   180</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>               <td>     6</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Item_Preference</th> <td>   -0.7072</td> <td>    0.078</td> <td>   -9.051</td> <td> 0.000</td> <td>   -0.860</td> <td>   -0.554</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Health_Behavior</th> <td>   -0.4027</td> <td>    0.173</td> <td>   -2.332</td> <td> 0.020</td> <td>   -0.741</td> <td>   -0.064</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>1/2</th>             <td>   -6.9326</td> <td>    0.809</td> <td>   -8.569</td> <td> 0.000</td> <td>   -8.518</td> <td>   -5.347</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>2/3</th>             <td>    0.6917</td> <td>    0.120</td> <td>    5.748</td> <td> 0.000</td> <td>    0.456</td> <td>    0.928</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>3/4</th>             <td>    0.4566</td> <td>    0.155</td> <td>    2.953</td> <td> 0.003</td> <td>    0.154</td> <td>    0.760</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>4/5</th>             <td>    0.2288</td> <td>    0.222</td> <td>    1.029</td> <td> 0.303</td> <td>   -0.207</td> <td>    0.665</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Oe61065o9Hum"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}