Hypothesis_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be08a110",
   "metadata": {},
   "source": [
    "## <span style = \"color:#1A237E;\">Hypothesis Five</span>\n",
    "### <span style = \"color:green;\">Null Hypothesis</span>\n",
    "The distribution of the poor is not correlated with credit access.\n",
    "### <span style = \"color:green;\">Alternative Hypothesis</span>\n",
    "The distribution of the poor is correlated with credit access.\n",
    "### <span style = \"color:green;\">Relevance</span>\n",
    "Poverty is distributed unevenly across different parts of Kenya. This hypothesis seeks to explore whether there is a relationship between the distribution of poverty and credit access in Kenya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d22e4b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "666b32bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>residence_county</th>\n",
       "      <th>Headcount Rate (%)</th>\n",
       "      <th>Distribution of the Poor (%)</th>\n",
       "      <th>Poverty Gap (%)</th>\n",
       "      <th>Severity of Poverty (%)</th>\n",
       "      <th>Population (ths)</th>\n",
       "      <th>Number of Poor (ths)</th>\n",
       "      <th>Proportion of households that sought credit (%)</th>\n",
       "      <th>Number of Households (ths)</th>\n",
       "      <th>Proportion of households that sought and accessed credit (%)</th>\n",
       "      <th>Number of Households that sought credit (ths)</th>\n",
       "      <th>Distribution of the Poor (%).1</th>\n",
       "      <th>Poverty Gap (%).1</th>\n",
       "      <th>Severity of Poverty (%).1</th>\n",
       "      <th>Population (ths).1</th>\n",
       "      <th>Number of Poor (ths).1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baringo</td>\n",
       "      <td>39.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>704</td>\n",
       "      <td>278</td>\n",
       "      <td>44.4</td>\n",
       "      <td>152</td>\n",
       "      <td>98.6</td>\n",
       "      <td>68</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>704</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bomet</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>916</td>\n",
       "      <td>447</td>\n",
       "      <td>19.5</td>\n",
       "      <td>179</td>\n",
       "      <td>83.8</td>\n",
       "      <td>35</td>\n",
       "      <td>2.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>916</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bungoma</td>\n",
       "      <td>35.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1,553</td>\n",
       "      <td>555</td>\n",
       "      <td>32.8</td>\n",
       "      <td>321</td>\n",
       "      <td>58.0</td>\n",
       "      <td>105</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1,553</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Busia</td>\n",
       "      <td>69.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>22.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>840</td>\n",
       "      <td>583</td>\n",
       "      <td>5.5</td>\n",
       "      <td>177</td>\n",
       "      <td>62.2</td>\n",
       "      <td>10</td>\n",
       "      <td>3.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>840</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elgeyo/Marakwet</td>\n",
       "      <td>43.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>469</td>\n",
       "      <td>204</td>\n",
       "      <td>43.1</td>\n",
       "      <td>99</td>\n",
       "      <td>98.7</td>\n",
       "      <td>43</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>469</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  residence_county  Headcount Rate (%)  Distribution of the Poor (%)  \\\n",
       "0          Baringo                39.6                           1.7   \n",
       "1            Bomet                48.8                           2.7   \n",
       "2          Bungoma                35.7                           3.4   \n",
       "3            Busia                69.3                           3.6   \n",
       "4  Elgeyo/Marakwet                43.4                           1.2   \n",
       "\n",
       "   Poverty Gap (%)  Severity of Poverty (%) Population (ths)  \\\n",
       "0              9.7                      4.2              704   \n",
       "1              9.3                      2.8              916   \n",
       "2              9.5                      3.6            1,553   \n",
       "3             22.3                      9.3              840   \n",
       "4             13.4                      5.6              469   \n",
       "\n",
       "   Number of Poor (ths)  Proportion of households that sought credit (%)  \\\n",
       "0                   278                                             44.4   \n",
       "1                   447                                             19.5   \n",
       "2                   555                                             32.8   \n",
       "3                   583                                              5.5   \n",
       "4                   204                                             43.1   \n",
       "\n",
       "   Number of Households (ths)  \\\n",
       "0                         152   \n",
       "1                         179   \n",
       "2                         321   \n",
       "3                         177   \n",
       "4                          99   \n",
       "\n",
       "   Proportion of households that sought and accessed credit (%)  \\\n",
       "0                                               98.6              \n",
       "1                                               83.8              \n",
       "2                                               58.0              \n",
       "3                                               62.2              \n",
       "4                                               98.7              \n",
       "\n",
       "   Number of Households that sought credit (ths)  \\\n",
       "0                                             68   \n",
       "1                                             35   \n",
       "2                                            105   \n",
       "3                                             10   \n",
       "4                                             43   \n",
       "\n",
       "   Distribution of the Poor (%).1  Poverty Gap (%).1  \\\n",
       "0                             2.0               10.8   \n",
       "1                             2.1                5.6   \n",
       "2                             3.5                9.5   \n",
       "3                             3.4               17.5   \n",
       "4                             1.4               10.8   \n",
       "\n",
       "   Severity of Poverty (%).1 Population (ths).1  Number of Poor (ths).1  \n",
       "0                        4.1                704                     291  \n",
       "1                        1.6                916                     300  \n",
       "2                        3.9              1,553                     503  \n",
       "3                        7.2                840                     500  \n",
       "4                        4.0                469                     210  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('overall_poverty_est.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "227b86ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Headcount Rate (%)</th>\n",
       "      <th>Distribution of the Poor (%)</th>\n",
       "      <th>Poverty Gap (%)</th>\n",
       "      <th>Severity of Poverty (%)</th>\n",
       "      <th>Number of Poor (ths)</th>\n",
       "      <th>Proportion of households that sought credit (%)</th>\n",
       "      <th>Number of Households (ths)</th>\n",
       "      <th>Proportion of households that sought and accessed credit (%)</th>\n",
       "      <th>Number of Households that sought credit (ths)</th>\n",
       "      <th>Distribution of the Poor (%).1</th>\n",
       "      <th>Poverty Gap (%).1</th>\n",
       "      <th>Severity of Poverty (%).1</th>\n",
       "      <th>Number of Poor (ths).1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.557447</td>\n",
       "      <td>2.129787</td>\n",
       "      <td>12.085106</td>\n",
       "      <td>5.306383</td>\n",
       "      <td>349.042553</td>\n",
       "      <td>33.082979</td>\n",
       "      <td>242.808511</td>\n",
       "      <td>85.814894</td>\n",
       "      <td>81.851064</td>\n",
       "      <td>2.127660</td>\n",
       "      <td>10.314894</td>\n",
       "      <td>4.463830</td>\n",
       "      <td>309.297872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.291085</td>\n",
       "      <td>1.109429</td>\n",
       "      <td>8.496751</td>\n",
       "      <td>5.254911</td>\n",
       "      <td>182.080125</td>\n",
       "      <td>16.187170</td>\n",
       "      <td>225.060343</td>\n",
       "      <td>16.561138</td>\n",
       "      <td>79.732972</td>\n",
       "      <td>1.142404</td>\n",
       "      <td>6.053852</td>\n",
       "      <td>3.569827</td>\n",
       "      <td>166.793795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.700000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.800000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>7.350000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>84.050000</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.450000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>455.500000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>277.500000</td>\n",
       "      <td>97.550000</td>\n",
       "      <td>108.500000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>385.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.400000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>66.100000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>99.200000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>717.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Headcount Rate (%)  Distribution of the Poor (%)  Poverty Gap (%)  \\\n",
       "count           47.000000                     47.000000        47.000000   \n",
       "mean            40.557447                      2.129787        12.085106   \n",
       "std             16.291085                      1.109429         8.496751   \n",
       "min             16.700000                      0.200000         2.400000   \n",
       "25%             28.800000                      1.400000         7.350000   \n",
       "50%             35.800000                      2.000000         9.400000   \n",
       "75%             47.450000                      2.750000        13.400000   \n",
       "max             79.400000                      5.200000        46.000000   \n",
       "\n",
       "       Severity of Poverty (%)  Number of Poor (ths)  \\\n",
       "count                47.000000             47.000000   \n",
       "mean                  5.306383            349.042553   \n",
       "std                   5.254911            182.080125   \n",
       "min                   0.500000             36.000000   \n",
       "25%                   2.500000            231.000000   \n",
       "50%                   3.500000            321.000000   \n",
       "75%                   5.800000            455.500000   \n",
       "max                  30.800000            860.000000   \n",
       "\n",
       "       Proportion of households that sought credit (%)  \\\n",
       "count                                        47.000000   \n",
       "mean                                         33.082979   \n",
       "std                                          16.187170   \n",
       "min                                           5.500000   \n",
       "25%                                          21.300000   \n",
       "50%                                          32.900000   \n",
       "75%                                          43.100000   \n",
       "max                                          66.100000   \n",
       "\n",
       "       Number of Households (ths)  \\\n",
       "count                   47.000000   \n",
       "mean                   242.808511   \n",
       "std                    225.060343   \n",
       "min                     30.000000   \n",
       "25%                    127.000000   \n",
       "50%                    210.000000   \n",
       "75%                    277.500000   \n",
       "max                   1503.000000   \n",
       "\n",
       "       Proportion of households that sought and accessed credit (%)  \\\n",
       "count                                          47.000000              \n",
       "mean                                           85.814894              \n",
       "std                                            16.561138              \n",
       "min                                            33.900000              \n",
       "25%                                            84.050000              \n",
       "50%                                            92.500000              \n",
       "75%                                            97.550000              \n",
       "max                                            99.200000              \n",
       "\n",
       "       Number of Households that sought credit (ths)  \\\n",
       "count                                      47.000000   \n",
       "mean                                       81.851064   \n",
       "std                                        79.732972   \n",
       "min                                         4.000000   \n",
       "25%                                        39.500000   \n",
       "50%                                        69.000000   \n",
       "75%                                       108.500000   \n",
       "max                                       510.000000   \n",
       "\n",
       "       Distribution of the Poor (%).1  Poverty Gap (%).1  \\\n",
       "count                       47.000000          47.000000   \n",
       "mean                         2.127660          10.314894   \n",
       "std                          1.142404           6.053852   \n",
       "min                          0.200000           3.000000   \n",
       "25%                          1.300000           6.750000   \n",
       "50%                          2.000000           9.100000   \n",
       "75%                          2.650000          11.750000   \n",
       "max                          4.900000          32.900000   \n",
       "\n",
       "       Severity of Poverty (%).1  Number of Poor (ths).1  \n",
       "count                  47.000000               47.000000  \n",
       "mean                    4.463830              309.297872  \n",
       "std                     3.569827              166.793795  \n",
       "min                     0.800000               25.000000  \n",
       "25%                     2.550000              192.000000  \n",
       "50%                     3.500000              287.000000  \n",
       "75%                     4.950000              385.500000  \n",
       "max                    20.400000              717.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display statistical summary\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bca5d95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display number of  rows and columns\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "029763bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['residence_county', 'Headcount Rate (%)',\n",
       "       'Distribution of the Poor (%)', 'Poverty Gap (%)',\n",
       "       'Severity of Poverty (%)', 'Population (ths)', 'Number of Poor (ths)',\n",
       "       'Proportion of households that sought credit (%)',\n",
       "       'Number of Households (ths)',\n",
       "       'Proportion of households that sought and accessed credit (%)',\n",
       "       'Number of Households that sought credit (ths)',\n",
       "       'Distribution of the Poor (%).1', 'Poverty Gap (%).1',\n",
       "       'Severity of Poverty (%).1', 'Population (ths).1',\n",
       "       'Number of Poor (ths).1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display column headers\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6ac2bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize correlation between variables\n",
    "sns.pairplot(data=data, vars=[\"Distribution of the Poor (%)\", \"Proportion of households that sought and accessed credit (%)\"],\n",
    "             height=1.5, aspect=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32c350e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize distribution of the poor\n",
    "sns.histplot(data=data, x=\"Distribution of the Poor (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "301f6153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# visualize credit access\n",
    "sns.histplot(data=data, x=\"Proportion of households that sought and accessed credit (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2392a2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the correlation coefficient and p-value\n",
    "corr, p_value = pearsonr(\n",
    "    data['Proportion of households that sought and accessed credit (%)'], \n",
    "    data['Distribution of the Poor (%)']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f083aad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.00516950964944148\n",
      "p-value: 0.9724894071604597\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "print('Correlation coefficient:', corr)\n",
    "print('p-value:', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebcd03e0",
   "metadata": {},
   "source": [
    "### <span style = \"color:green;\">Observations and Inferences</span>\n",
    "\n",
    "* A correlation coefficient of `0.005` indicates _a very weak positive correlation_ between the two \n",
    "variables, **while** the p-value of `0.972` suggests that _the correlation is not statistically significant_ \n",
    "at the standard significance level of `0.05`. \n",
    "* This means that there is no evidence to suggest that the two variables are related to each other."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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