05.07.Multiple_linear_regression_Exercise.ipynb

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    "<div align=\"center\" style=\" font-size: 80%; text-align: center; margin: 0 auto\">\n",
    "<img src=\"https://raw.githubusercontent.com/Explore-AI/Pictures/master/Python-Notebook-Banners/Exercise.png\"  style=\"display: block; margin-left: auto; margin-right: auto;\";/>\n",
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   "source": [
    "# Exercise: Multiple linear regression\n",
    "© ExploreAI Academy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26af890c",
   "metadata": {},
   "source": [
    "In this exercise, we will reinforce our knowledge of the fundamental concepts of multiple linear regression by implementing  an MLR model and assessing its accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "tags": []
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   "source": [
    "## Learning objectives\n",
    "\n",
    "By the end of this train, you should be able to:\n",
    "* Implement multiple linear regression in Python\n",
    "* Assess model accuracy by interpreting outputs\n",
    "* Understand what is meant by linearity and multicollinearity of predictors\n",
    "* Understand what is meant by independence, homoscedasticity and normality of residuals and how we can check these in our model output\n"
   ]
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    "## The dataset\n",
    "\n",
    "For this exerecise, we make use of the `enviro_indicators` dataset. The dataset comprises socio-economic and environmental data collected from various countries, focusing on factors related to sustainable development. It includes information such as forest coverage, biodiversity index, protected areas, rural population, and deforestation rates, aimed at understanding the relationship between human activities and land degradation.\n",
    "\n",
    "The following are the descriptions of the columns in our dataset: \n",
    "\n",
    "* `country`: Name of the country.\n",
    "* `forest_coverage`: Percentage of land covered by forests. This indicator represents the extent of forested areas within the total land area of each country.\n",
    "* `biodiversity_index`: Index representing the biodiversity level in each country. This indicator provides a measure of the variety and variability of organisms within each country's ecosystem.\n",
    "* `protected_areas`: Percentage of land designated as protected areas. This indicator reflects the proportion of land that is legally protected and managed for conservation purposes.\n",
    "* `deforestation_rate`: Annual rate of deforestation (%). This indicator quantifies the percentage of forested land that is lost each year due to deforestation activities.\n",
    "* `carbon_sequestration`: Amount of carbon sequestered by forests (metric tons per hectare). This indicator measures the capacity of forests to absorb and store carbon dioxide from the atmosphere, thus helping mitigate climate change.\n",
    "* `soil_erosion`: Rate of soil erosion (tons per hectare per year). This indicator indicates the amount of soil that is lost or displaced annually due to erosion, which can have detrimental effects on land productivity and ecosystem health.\n",
    "* `land_degradation`: Percentage of land classified as degraded. This indicator represents the proportion of land that has suffered a decline in its ecological condition, often resulting from unsustainable land use practices.\n",
    "* `rural_population`: Percentage of population living in rural areas. This indicator reflects the proportion of a country's population residing in rural areas rather than urban centers.\n",
    "* `population_density`: Population density (people per square kilometer). This indicator quantifies the average number of people living within a unit area of land, providing insights into the distribution of human populations across different countries.\n",
    "\n",
    "\n",
    "We begin by importing some libraries which will help us load and explore our data. "
   ]
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  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.graphics.correlation as sgc\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "import statsmodels.stats.api as sms\n",
    "from statsmodels.stats.outliers_influence import OLSInfluence as influence"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>forest_coverage</th>\n",
       "      <th>biodiversity_index</th>\n",
       "      <th>protected_areas</th>\n",
       "      <th>deforestation_rate</th>\n",
       "      <th>carbon_sequestration</th>\n",
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       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>36.217808</td>\n",
       "      <td>6.505159</td>\n",
       "      <td>14.832708</td>\n",
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       "      <td>35.912142</td>\n",
       "      <td>468.715839</td>\n",
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       "      <th>Guinea-Bissau</th>\n",
       "      <td>76.550001</td>\n",
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       "      <td>123.384939</td>\n",
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       "      <td>34.639379</td>\n",
       "      <td>351.054600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>61.239576</td>\n",
       "      <td>96.563203</td>\n",
       "      <td>9.932348</td>\n",
       "      <td>115.743745</td>\n",
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       "      <td>26.951490</td>\n",
       "      <td>78.380633</td>\n",
       "      <td>289.329973</td>\n",
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       "      <th>Lesotho</th>\n",
       "      <td>51.906094</td>\n",
       "      <td>80.839735</td>\n",
       "      <td>33.076894</td>\n",
       "      <td>111.752369</td>\n",
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       "      <td>11.179719</td>\n",
       "      <td>27.460479</td>\n",
       "      <td>43.585863</td>\n",
       "      <td>57.616482</td>\n",
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       "      <th>Indonesia</th>\n",
       "      <td>20.921305</td>\n",
       "      <td>30.461377</td>\n",
       "      <td>7.609273</td>\n",
       "      <td>46.450326</td>\n",
       "      <td>103.939415</td>\n",
       "      <td>2.715506</td>\n",
       "      <td>46.297784</td>\n",
       "      <td>73.522793</td>\n",
       "      <td>311.353541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>20.919616</td>\n",
       "      <td>9.767211</td>\n",
       "      <td>39.541043</td>\n",
       "      <td>25.249501</td>\n",
       "      <td>201.926827</td>\n",
       "      <td>16.870747</td>\n",
       "      <td>54.855513</td>\n",
       "      <td>57.868318</td>\n",
       "      <td>495.126387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Lucia</th>\n",
       "      <td>14.065853</td>\n",
       "      <td>68.423303</td>\n",
       "      <td>32.028567</td>\n",
       "      <td>76.130581</td>\n",
       "      <td>474.309367</td>\n",
       "      <td>7.094821</td>\n",
       "      <td>54.354321</td>\n",
       "      <td>67.688678</td>\n",
       "      <td>78.641167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>70.632330</td>\n",
       "      <td>44.015249</td>\n",
       "      <td>11.955049</td>\n",
       "      <td>73.358093</td>\n",
       "      <td>195.441319</td>\n",
       "      <td>4.543852</td>\n",
       "      <td>48.993777</td>\n",
       "      <td>50.158226</td>\n",
       "      <td>263.981530</td>\n",
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       "      <th>Kenya</th>\n",
       "      <td>52.078051</td>\n",
       "      <td>12.203823</td>\n",
       "      <td>5.193274</td>\n",
       "      <td>36.843972</td>\n",
       "      <td>283.455780</td>\n",
       "      <td>1.774728</td>\n",
       "      <td>42.101582</td>\n",
       "      <td>54.614233</td>\n",
       "      <td>439.912805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>59.565080</td>\n",
       "      <td>49.517691</td>\n",
       "      <td>33.541150</td>\n",
       "      <td>81.512031</td>\n",
       "      <td>366.358532</td>\n",
       "      <td>12.226966</td>\n",
       "      <td>14.206998</td>\n",
       "      <td>49.551062</td>\n",
       "      <td>372.976623</td>\n",
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       "                        forest_coverage  biodiversity_index  protected_areas  \\\n",
       "Country                                                                        \n",
       "Vietnam                       36.217808            6.505159        14.832708   \n",
       "Guinea-Bissau                 76.550001           94.888554        23.994363   \n",
       "Bosnia and Herzegovina        61.239576           96.563203         9.932348   \n",
       "Lesotho                       51.906094           80.839735        33.076894   \n",
       "Indonesia                     20.921305           30.461377         7.609273   \n",
       "Singapore                     20.919616            9.767211        39.541043   \n",
       "Saint Lucia                   14.065853           68.423303        32.028567   \n",
       "Turkmenistan                  70.632330           44.015249        11.955049   \n",
       "Kenya                         52.078051           12.203823         5.193274   \n",
       "United States                 59.565080           49.517691        33.541150   \n",
       "\n",
       "                        deforestation_rate  carbon_sequestration  \\\n",
       "Country                                                            \n",
       "Vietnam                          36.583106             53.128459   \n",
       "Guinea-Bissau                   123.384939            279.836286   \n",
       "Bosnia and Herzegovina          115.743745            237.834951   \n",
       "Lesotho                         111.752369            149.948515   \n",
       "Indonesia                        46.450326            103.939415   \n",
       "Singapore                        25.249501            201.926827   \n",
       "Saint Lucia                      76.130581            474.309367   \n",
       "Turkmenistan                     73.358093            195.441319   \n",
       "Kenya                            36.843972            283.455780   \n",
       "United States                    81.512031            366.358532   \n",
       "\n",
       "                        soil_erosion  land_degradation  rural_population  \\\n",
       "Country                                                                    \n",
       "Vietnam                     7.987880         55.020903         35.912142   \n",
       "Guinea-Bissau              13.013811         41.655073         34.639379   \n",
       "Bosnia and Herzegovina     13.037065         26.951490         78.380633   \n",
       "Lesotho                    11.179719         27.460479         43.585863   \n",
       "Indonesia                   2.715506         46.297784         73.522793   \n",
       "Singapore                  16.870747         54.855513         57.868318   \n",
       "Saint Lucia                 7.094821         54.354321         67.688678   \n",
       "Turkmenistan                4.543852         48.993777         50.158226   \n",
       "Kenya                       1.774728         42.101582         54.614233   \n",
       "United States              12.226966         14.206998         49.551062   \n",
       "\n",
       "                        population_density  \n",
       "Country                                     \n",
       "Vietnam                         468.715839  \n",
       "Guinea-Bissau                   351.054600  \n",
       "Bosnia and Herzegovina          289.329973  \n",
       "Lesotho                          57.616482  \n",
       "Indonesia                       311.353541  \n",
       "Singapore                       495.126387  \n",
       "Saint Lucia                      78.641167  \n",
       "Turkmenistan                    263.981530  \n",
       "Kenya                           439.912805  \n",
       "United States                   372.976623  "
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   "source": [
    "# Read the data\n",
    "environmental_indicators = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/enviro_indicators.csv', index_col=0)\n",
    "environmental_indicators.head(10)"
   ]
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   "source": [
    "## Exercises\n",
    "\n",
    "As part of a conservation effort, we are tasked with building a predictive model to understand the factors influencing the biodiversity index across different countries. \n",
    "\n",
    "Use the flow of these exercises as a guide to build a **multiple linear regression model**. "
   ]
  },
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   "source": [
    "### Exercise 1\n",
    "\n",
    "Print the first few rows of the dataset, the summary statistics, and check for missing values. Doing this provides an overview of the structure of the data, and highlights whether there are any issues with missing data. "
   ]
  },
  {
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       "      <th>forest_coverage</th>\n",
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       "      <th>protected_areas</th>\n",
       "      <th>deforestation_rate</th>\n",
       "      <th>carbon_sequestration</th>\n",
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       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "      <th>Vietnam</th>\n",
       "      <td>36.217808</td>\n",
       "      <td>6.505159</td>\n",
       "      <td>14.832708</td>\n",
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       "      <th>Guinea-Bissau</th>\n",
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       "      <td>123.384939</td>\n",
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       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>61.239576</td>\n",
       "      <td>96.563203</td>\n",
       "      <td>9.932348</td>\n",
       "      <td>115.743745</td>\n",
       "      <td>237.834951</td>\n",
       "      <td>13.037065</td>\n",
       "      <td>26.951490</td>\n",
       "      <td>78.380633</td>\n",
       "      <td>289.329973</td>\n",
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       "      <th>Lesotho</th>\n",
       "      <td>51.906094</td>\n",
       "      <td>80.839735</td>\n",
       "      <td>33.076894</td>\n",
       "      <td>111.752369</td>\n",
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       "      <th>Indonesia</th>\n",
       "      <td>20.921305</td>\n",
       "      <td>30.461377</td>\n",
       "      <td>7.609273</td>\n",
       "      <td>46.450326</td>\n",
       "      <td>103.939415</td>\n",
       "      <td>2.715506</td>\n",
       "      <td>46.297784</td>\n",
       "      <td>73.522793</td>\n",
       "      <td>311.353541</td>\n",
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       "                        forest_coverage  biodiversity_index  protected_areas  \\\n",
       "Country                                                                        \n",
       "Vietnam                       36.217808            6.505159        14.832708   \n",
       "Guinea-Bissau                 76.550001           94.888554        23.994363   \n",
       "Bosnia and Herzegovina        61.239576           96.563203         9.932348   \n",
       "Lesotho                       51.906094           80.839735        33.076894   \n",
       "Indonesia                     20.921305           30.461377         7.609273   \n",
       "\n",
       "                        deforestation_rate  carbon_sequestration  \\\n",
       "Country                                                            \n",
       "Vietnam                          36.583106             53.128459   \n",
       "Guinea-Bissau                   123.384939            279.836286   \n",
       "Bosnia and Herzegovina          115.743745            237.834951   \n",
       "Lesotho                         111.752369            149.948515   \n",
       "Indonesia                        46.450326            103.939415   \n",
       "\n",
       "                        soil_erosion  land_degradation  rural_population  \\\n",
       "Country                                                                    \n",
       "Vietnam                     7.987880         55.020903         35.912142   \n",
       "Guinea-Bissau              13.013811         41.655073         34.639379   \n",
       "Bosnia and Herzegovina     13.037065         26.951490         78.380633   \n",
       "Lesotho                    11.179719         27.460479         43.585863   \n",
       "Indonesia                   2.715506         46.297784         73.522793   \n",
       "\n",
       "                        population_density  \n",
       "Country                                     \n",
       "Vietnam                         468.715839  \n",
       "Guinea-Bissau                   351.054600  \n",
       "Bosnia and Herzegovina          289.329973  \n",
       "Lesotho                          57.616482  \n",
       "Indonesia                       311.353541  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "environmental_indicators.head(5)\n"
   ]
  },
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       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>forest_coverage</th>\n",
       "      <td>32.0</td>\n",
       "      <td>40.484972</td>\n",
       "      <td>19.529573</td>\n",
       "      <td>11.440915</td>\n",
       "      <td>22.810674</td>\n",
       "      <td>38.226980</td>\n",
       "      <td>52.603530</td>\n",
       "      <td>77.893690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biodiversity_index</th>\n",
       "      <td>32.0</td>\n",
       "      <td>49.873676</td>\n",
       "      <td>32.201278</td>\n",
       "      <td>3.438852</td>\n",
       "      <td>24.308070</td>\n",
       "      <td>46.766470</td>\n",
       "      <td>81.348239</td>\n",
       "      <td>96.958463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protected_areas</th>\n",
       "      <td>32.0</td>\n",
       "      <td>22.542576</td>\n",
       "      <td>10.338851</td>\n",
       "      <td>5.193274</td>\n",
       "      <td>14.113293</td>\n",
       "      <td>24.319532</td>\n",
       "      <td>31.716570</td>\n",
       "      <td>39.541043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deforestation_rate</th>\n",
       "      <td>32.0</td>\n",
       "      <td>69.063387</td>\n",
       "      <td>30.100259</td>\n",
       "      <td>18.113759</td>\n",
       "      <td>46.131162</td>\n",
       "      <td>70.838794</td>\n",
       "      <td>90.632026</td>\n",
       "      <td>128.118420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbon_sequestration</th>\n",
       "      <td>32.0</td>\n",
       "      <td>255.649653</td>\n",
       "      <td>132.047729</td>\n",
       "      <td>53.128459</td>\n",
       "      <td>158.644367</td>\n",
       "      <td>225.734136</td>\n",
       "      <td>355.935380</td>\n",
       "      <td>493.542704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soil_erosion</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.874722</td>\n",
       "      <td>5.464372</td>\n",
       "      <td>1.315169</td>\n",
       "      <td>5.113528</td>\n",
       "      <td>10.896565</td>\n",
       "      <td>13.623203</td>\n",
       "      <td>18.797870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_degradation</th>\n",
       "      <td>32.0</td>\n",
       "      <td>35.263996</td>\n",
       "      <td>14.555348</td>\n",
       "      <td>10.253079</td>\n",
       "      <td>21.700207</td>\n",
       "      <td>40.988263</td>\n",
       "      <td>45.781167</td>\n",
       "      <td>55.020903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural_population</th>\n",
       "      <td>32.0</td>\n",
       "      <td>53.223641</td>\n",
       "      <td>18.552335</td>\n",
       "      <td>20.927397</td>\n",
       "      <td>37.461786</td>\n",
       "      <td>51.783151</td>\n",
       "      <td>71.766124</td>\n",
       "      <td>78.380633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>32.0</td>\n",
       "      <td>299.594850</td>\n",
       "      <td>129.851023</td>\n",
       "      <td>27.611714</td>\n",
       "      <td>227.116183</td>\n",
       "      <td>319.917934</td>\n",
       "      <td>402.520220</td>\n",
       "      <td>495.126387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count        mean         std        min         25%  \\\n",
       "forest_coverage        32.0   40.484972   19.529573  11.440915   22.810674   \n",
       "biodiversity_index     32.0   49.873676   32.201278   3.438852   24.308070   \n",
       "protected_areas        32.0   22.542576   10.338851   5.193274   14.113293   \n",
       "deforestation_rate     32.0   69.063387   30.100259  18.113759   46.131162   \n",
       "carbon_sequestration   32.0  255.649653  132.047729  53.128459  158.644367   \n",
       "soil_erosion           32.0    9.874722    5.464372   1.315169    5.113528   \n",
       "land_degradation       32.0   35.263996   14.555348  10.253079   21.700207   \n",
       "rural_population       32.0   53.223641   18.552335  20.927397   37.461786   \n",
       "population_density     32.0  299.594850  129.851023  27.611714  227.116183   \n",
       "\n",
       "                             50%         75%         max  \n",
       "forest_coverage        38.226980   52.603530   77.893690  \n",
       "biodiversity_index     46.766470   81.348239   96.958463  \n",
       "protected_areas        24.319532   31.716570   39.541043  \n",
       "deforestation_rate     70.838794   90.632026  128.118420  \n",
       "carbon_sequestration  225.734136  355.935380  493.542704  \n",
       "soil_erosion           10.896565   13.623203   18.797870  \n",
       "land_degradation       40.988263   45.781167   55.020903  \n",
       "rural_population       51.783151   71.766124   78.380633  \n",
       "population_density    319.917934  402.520220  495.126387  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "environmental_indicators.describe().T"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "98efc31e",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "Create a correlation matrix using the columns in the data. We do this to see whether there are potentially strong relationships between the independant variables and the dependant variable, but it can also be used to investigate potential multicollinearity between the independent variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d4da463",
   "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>forest_coverage</th>\n",
       "      <th>biodiversity_index</th>\n",
       "      <th>protected_areas</th>\n",
       "      <th>deforestation_rate</th>\n",
       "      <th>carbon_sequestration</th>\n",
       "      <th>soil_erosion</th>\n",
       "      <th>land_degradation</th>\n",
       "      <th>rural_population</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>forest_coverage</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274022</td>\n",
       "      <td>0.108666</td>\n",
       "      <td>0.556136</td>\n",
       "      <td>0.053950</td>\n",
       "      <td>-0.109275</td>\n",
       "      <td>0.154287</td>\n",
       "      <td>0.022843</td>\n",
       "      <td>0.027884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biodiversity_index</th>\n",
       "      <td>0.274022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067212</td>\n",
       "      <td>0.922463</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>-0.062205</td>\n",
       "      <td>-0.136184</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>-0.135042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protected_areas</th>\n",
       "      <td>0.108666</td>\n",
       "      <td>-0.067212</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120202</td>\n",
       "      <td>0.146215</td>\n",
       "      <td>0.321587</td>\n",
       "      <td>-0.113558</td>\n",
       "      <td>0.172193</td>\n",
       "      <td>-0.001360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deforestation_rate</th>\n",
       "      <td>0.556136</td>\n",
       "      <td>0.922463</td>\n",
       "      <td>0.120202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063443</td>\n",
       "      <td>-0.082024</td>\n",
       "      <td>-0.092885</td>\n",
       "      <td>0.105760</td>\n",
       "      <td>-0.126157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbon_sequestration</th>\n",
       "      <td>0.053950</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>0.146215</td>\n",
       "      <td>0.063443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.237919</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>-0.017830</td>\n",
       "      <td>-0.344947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soil_erosion</th>\n",
       "      <td>-0.109275</td>\n",
       "      <td>-0.062205</td>\n",
       "      <td>0.321587</td>\n",
       "      <td>-0.082024</td>\n",
       "      <td>0.237919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110673</td>\n",
       "      <td>0.146636</td>\n",
       "      <td>0.046528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_degradation</th>\n",
       "      <td>0.154287</td>\n",
       "      <td>-0.136184</td>\n",
       "      <td>-0.113558</td>\n",
       "      <td>-0.092885</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>-0.110673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085273</td>\n",
       "      <td>-0.137509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural_population</th>\n",
       "      <td>0.022843</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>0.172193</td>\n",
       "      <td>0.105760</td>\n",
       "      <td>-0.017830</td>\n",
       "      <td>0.146636</td>\n",
       "      <td>-0.085273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>0.027884</td>\n",
       "      <td>-0.135042</td>\n",
       "      <td>-0.001360</td>\n",
       "      <td>-0.126157</td>\n",
       "      <td>-0.344947</td>\n",
       "      <td>0.046528</td>\n",
       "      <td>-0.137509</td>\n",
       "      <td>0.226600</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      forest_coverage  biodiversity_index  protected_areas  \\\n",
       "forest_coverage              1.000000            0.274022         0.108666   \n",
       "biodiversity_index           0.274022            1.000000        -0.067212   \n",
       "protected_areas              0.108666           -0.067212         1.000000   \n",
       "deforestation_rate           0.556136            0.922463         0.120202   \n",
       "carbon_sequestration         0.053950            0.009418         0.146215   \n",
       "soil_erosion                -0.109275           -0.062205         0.321587   \n",
       "land_degradation             0.154287           -0.136184        -0.113558   \n",
       "rural_population             0.022843            0.042478         0.172193   \n",
       "population_density           0.027884           -0.135042        -0.001360   \n",
       "\n",
       "                      deforestation_rate  carbon_sequestration  soil_erosion  \\\n",
       "forest_coverage                 0.556136              0.053950     -0.109275   \n",
       "biodiversity_index              0.922463              0.009418     -0.062205   \n",
       "protected_areas                 0.120202              0.146215      0.321587   \n",
       "deforestation_rate              1.000000              0.063443     -0.082024   \n",
       "carbon_sequestration            0.063443              1.000000      0.237919   \n",
       "soil_erosion                   -0.082024              0.237919      1.000000   \n",
       "land_degradation               -0.092885              0.268428     -0.110673   \n",
       "rural_population                0.105760             -0.017830      0.146636   \n",
       "population_density             -0.126157             -0.344947      0.046528   \n",
       "\n",
       "                      land_degradation  rural_population  population_density  \n",
       "forest_coverage               0.154287          0.022843            0.027884  \n",
       "biodiversity_index           -0.136184          0.042478           -0.135042  \n",
       "protected_areas              -0.113558          0.172193           -0.001360  \n",
       "deforestation_rate           -0.092885          0.105760           -0.126157  \n",
       "carbon_sequestration          0.268428         -0.017830           -0.344947  \n",
       "soil_erosion                 -0.110673          0.146636            0.046528  \n",
       "land_degradation              1.000000         -0.085273           -0.137509  \n",
       "rural_population             -0.085273          1.000000            0.226600  \n",
       "population_density           -0.137509          0.226600            1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = environmental_indicators.corr()\n",
    "corr"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84632575",
   "metadata": {},
   "source": [
    "Looking at the matrix result, which variables appear to be related? \n",
    "\n",
    "It is, however, difficult to view the data well in table format. It might be more useful to display it as a heatmap.\n",
    "\n",
    "1) Use the `statsmodels.graphics.correlation` package to show the correlation heatmap for the variables. \n",
    "2) What relationships exist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35d92134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.correlation import plot_corr\n",
    "plot_corr(corr, xnames=corr.index)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6f2c6b3d",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "Knowing what we know about the relationships between the variables, it's time to build our regression model. To get started, include all the numeric variables in the model. Remember that `biodiversity_index` is the dependent variable.\n",
    "\n",
    "Print the summary of the fitted model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f25f3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifying the variables of interest\n",
    "independent_cols = ['forest_coverage',  'protected_areas',\n",
    "      'deforestation_rate', 'carbon_sequestration', 'soil_erosion',\n",
    "      'land_degradation', 'rural_population', 'population_density']\n",
    "\n",
    "dependent_var = 'biodiversity_index'\n",
    "\n",
    "\n",
    "formula_str = dependent_var + \" ~ \" + \"+\".join(independent_cols)\n",
    "formula_str\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "model = sm.ols(formula= formula_str, data=environmental_indicators)\n",
    "\n",
    "fitted = model.fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a30f6b10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     biodiversity_index   R-squared:                       0.967\n",
      "Model:                            OLS   Adj. R-squared:                  0.956\n",
      "Method:                 Least Squares   F-statistic:                     85.25\n",
      "Date:                Tue, 04 Jun 2024   Prob (F-statistic):           2.99e-15\n",
      "Time:                        13:35:32   Log-Likelihood:                -101.24\n",
      "No. Observations:                  32   AIC:                             220.5\n",
      "Df Residuals:                      23   BIC:                             233.7\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept                0.2536      7.169      0.035      0.972     -14.577      15.084\n",
      "forest_coverage         -0.5592      0.079     -7.083      0.000      -0.723      -0.396\n",
      "protected_areas         -0.5416      0.128     -4.235      0.000      -0.806      -0.277\n",
      "deforestation_rate       1.2299      0.052     23.864      0.000       1.123       1.337\n",
      "carbon_sequestration    -0.0087      0.011     -0.813      0.425      -0.031       0.013\n",
      "soil_erosion             0.4042      0.248      1.627      0.117      -0.110       0.918\n",
      "land_degradation         0.0403      0.092      0.436      0.667      -0.151       0.232\n",
      "rural_population        -0.0951      0.069     -1.373      0.183      -0.238       0.048\n",
      "population_density       0.0046      0.011      0.439      0.664      -0.017       0.026\n",
      "==============================================================================\n",
      "Omnibus:                        0.520   Durbin-Watson:                   1.566\n",
      "Prob(Omnibus):                  0.771   Jarque-Bera (JB):                0.053\n",
      "Skew:                          -0.066   Prob(JB):                        0.974\n",
      "Kurtosis:                       3.149   Cond. No.                     2.52e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.52e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(fitted.summary())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12e1236a",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "\n",
    "The output from this model seems somewhat strange, with a comment warning of probable multicollinearity. Furthermore, some of the coefficient p-values are rather high (larger than expected). It may be worthwhile to consider which variables appear to be associated with the dependent variable, maintaining those, and possibly removing a few additional factors where there is potential for multicollinearity.\n",
    "\n",
    "Create another version of the model with fewer independent variables and see if the warning can be silenced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc05991e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     biodiversity_index   R-squared:                       0.965\n",
      "Model:                            OLS   Adj. R-squared:                  0.956\n",
      "Method:                 Least Squares   F-statistic:                     113.8\n",
      "Date:                Tue, 04 Jun 2024   Prob (F-statistic):           6.47e-17\n",
      "Time:                        13:35:33   Log-Likelihood:                -102.51\n",
      "No. Observations:                  32   AIC:                             219.0\n",
      "Df Residuals:                      25   BIC:                             229.3\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept               -2.0553      5.572     -0.369      0.715     -13.531       9.421\n",
      "forest_coverage         -0.5498      0.078     -7.089      0.000      -0.710      -0.390\n",
      "protected_areas         -0.5632      0.127     -4.447      0.000      -0.824      -0.302\n",
      "deforestation_rate       1.2185      0.050     24.337      0.000       1.115       1.322\n",
      "carbon_sequestration    -0.0094      0.010     -0.935      0.359      -0.030       0.011\n",
      "soil_erosion             0.3776      0.245      1.540      0.136      -0.127       0.882\n",
      "land_degradation         0.0397      0.092      0.432      0.670      -0.150       0.229\n",
      "==============================================================================\n",
      "Omnibus:                        1.230   Durbin-Watson:                   1.665\n",
      "Prob(Omnibus):                  0.541   Jarque-Bera (JB):                0.366\n",
      "Skew:                           0.135   Prob(JB):                        0.833\n",
      "Kurtosis:                       3.449   Cond. No.                     1.40e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.4e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "independent_cols2 = [\n",
    "      'forest_coverage',  'protected_areas'\n",
    "      , 'deforestation_rate', 'carbon_sequestration'\n",
    "      , 'soil_erosion', 'land_degradation'\n",
    "      # , 'rural_population', 'population_density'\n",
    "      ]\n",
    "\n",
    "dependent_var = 'biodiversity_index'\n",
    "\n",
    "\n",
    "formula_str = dependent_var + \" ~ \" + \"+\".join(independent_cols2)\n",
    "formula_str\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "model2 = sm.ols(formula= formula_str, data=environmental_indicators)\n",
    "\n",
    "fitted2 = model2.fit()\n",
    "\n",
    "print(fitted2.summary())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7973d4d7",
   "metadata": {},
   "source": [
    "### Exercise 5\n",
    "\n",
    "Evaluate the accuracy and reliability of the multiple linear regression model by conducting various diagnostic tests.\n",
    "\n",
    "Check for linearity by visually inspecting scatter plots between each independent variable and the biodiversity index. This will help in understanding whether the relationship between the independent variables and the biodiversity index is linear.\n",
    "\n",
    "What can we conclude from the charts? Are there any linear relationships visible? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b2da431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize= (16, 6))\n",
    "axs = axs.ravel()\n",
    "\n",
    "for index, column in enumerate(independent_cols):\n",
    "  axs[index].set_title(\"{} vs. bidiversity index\".format(column))\n",
    "  axs[index].scatter(x= environmental_indicators[column],\\\n",
    "                      y= environmental_indicators[dependent_var], color= 'blue')\n",
    "fig.tight_layout(pad=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd845dd",
   "metadata": {},
   "source": [
    "### Exercise 6\n",
    "Visualise the relationship between predictor variables and model residuals to assess the independence of residuals using scatter plots. Do we see any obvious dependence of residuals? How would we know?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a291fd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize= (12, 6))\n",
    "fig.suptitle('Predictor variables vs. model residuals')\n",
    "fig.supylabel(\"Resduals\")\n",
    "axs = axs.ravel()\n",
    "\n",
    "for index, column in enumerate(independent_cols):\n",
    "  axs[index].set_title(\"{}\".format(column))\n",
    "  axs[index].scatter(x= environmental_indicators[column],\\\n",
    "                      y= fitted.resid, color= 'blue')\n",
    "  axs[index].hlines(y= 0,\\\n",
    "                    xmin= environmental_indicators[column].min()*0.8,\\\n",
    "                    xmax= environmental_indicators[column].max()*1.2,\n",
    "                    color = 'red', linestyle= '--')\n",
    "fig.tight_layout(pad=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c66410e",
   "metadata": {},
   "source": [
    "### Exercise 7\n",
    "\n",
    "Check for homoscedasticity by conducting a `Breusch-Pagan` test on our model. Interpret the results to determine whether homoscedasticity could possibly be satisfied for our regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82ed562f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x2613b000800>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "plt.scatter(x= fitted.fittedvalues, y= fitted.resid)\n",
    "plt.hlines(y= 0, xmin= fitted.fittedvalues.min(),\\\n",
    "          xmax= fitted.fittedvalues.max(), colors='red', linestyles='-')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1b5e12",
   "metadata": {},
   "source": [
    "### Exercise 8\n",
    "Check for the normality of residuals by creating a Q-Q plot of the normalised residuals to visually assess their normality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "019f8041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 700x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "qqplot(fitted.resid_pearson, line= '45', fit= 'True')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8842ca",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "\n",
    "To ensure the accuracy of our regression model, it's crucial to check for outliers that might significantly affect the model's coefficients and predictions. Use Cook's distance to visually inspect a plot of residuals vs. fitted values by identifying potential outliers. \n",
    "\n",
    "Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26b1de3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inf = influence(fitted)\n",
    "\n",
    "(c, p) = inf.cooks_distance\n",
    "plt.plot(np.arange(len(c)), c, marker= 'o', linestyle= '-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9956efe",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42dba616",
   "metadata": {},
   "source": [
    "### Exercise 1\n",
    "\n",
    "Print the first few rows of the dataset, summary statistics, and test for missing values. Doing this provides an overview of the structure of the data, and highlights whether there are any issues with missing data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62341cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the dataset:\n",
      "                        forest_coverage  biodiversity_index  protected_areas  \\\n",
      "Country                                                                        \n",
      "Vietnam                       36.217808            6.505159        14.832708   \n",
      "Guinea-Bissau                 76.550001           94.888554        23.994363   \n",
      "Bosnia and Herzegovina        61.239576           96.563203         9.932348   \n",
      "Lesotho                       51.906094           80.839735        33.076894   \n",
      "Indonesia                     20.921305           30.461377         7.609273   \n",
      "\n",
      "                        deforestation_rate  carbon_sequestration  \\\n",
      "Country                                                            \n",
      "Vietnam                          36.583106             53.128459   \n",
      "Guinea-Bissau                   123.384939            279.836286   \n",
      "Bosnia and Herzegovina          115.743745            237.834951   \n",
      "Lesotho                         111.752369            149.948515   \n",
      "Indonesia                        46.450326            103.939415   \n",
      "\n",
      "                        soil_erosion  land_degradation  rural_population  \\\n",
      "Country                                                                    \n",
      "Vietnam                     7.987880         55.020903         35.912142   \n",
      "Guinea-Bissau              13.013811         41.655073         34.639379   \n",
      "Bosnia and Herzegovina     13.037065         26.951490         78.380633   \n",
      "Lesotho                    11.179719         27.460479         43.585863   \n",
      "Indonesia                   2.715506         46.297784         73.522793   \n",
      "\n",
      "                        population_density  \n",
      "Country                                     \n",
      "Vietnam                         468.715839  \n",
      "Guinea-Bissau                   351.054600  \n",
      "Bosnia and Herzegovina          289.329973  \n",
      "Lesotho                          57.616482  \n",
      "Indonesia                       311.353541  \n",
      "\n",
      "Summary statistics of the dataset:\n",
      "       forest_coverage  biodiversity_index  protected_areas  \\\n",
      "count        32.000000           32.000000        32.000000   \n",
      "mean         40.484972           49.873676        22.542576   \n",
      "std          19.529573           32.201278        10.338851   \n",
      "min          11.440915            3.438852         5.193274   \n",
      "25%          22.810674           24.308070        14.113293   \n",
      "50%          38.226980           46.766470        24.319532   \n",
      "75%          52.603530           81.348239        31.716570   \n",
      "max          77.893690           96.958463        39.541043   \n",
      "\n",
      "       deforestation_rate  carbon_sequestration  soil_erosion  \\\n",
      "count           32.000000             32.000000     32.000000   \n",
      "mean            69.063387            255.649653      9.874722   \n",
      "std             30.100259            132.047729      5.464372   \n",
      "min             18.113759             53.128459      1.315169   \n",
      "25%             46.131162            158.644367      5.113528   \n",
      "50%             70.838794            225.734136     10.896565   \n",
      "75%             90.632026            355.935380     13.623203   \n",
      "max            128.118420            493.542704     18.797870   \n",
      "\n",
      "       land_degradation  rural_population  population_density  \n",
      "count         32.000000         32.000000           32.000000  \n",
      "mean          35.263996         53.223641          299.594850  \n",
      "std           14.555348         18.552335          129.851023  \n",
      "min           10.253079         20.927397           27.611714  \n",
      "25%           21.700207         37.461786          227.116183  \n",
      "50%           40.988263         51.783151          319.917934  \n",
      "75%           45.781167         71.766124          402.520220  \n",
      "max           55.020903         78.380633          495.126387  \n",
      "\n",
      "Missing values in the dataset:\n",
      "forest_coverage         0\n",
      "biodiversity_index      0\n",
      "protected_areas         0\n",
      "deforestation_rate      0\n",
      "carbon_sequestration    0\n",
      "soil_erosion            0\n",
      "land_degradation        0\n",
      "rural_population        0\n",
      "population_density      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Print the first few rows of the dataset\n",
    "print(\"First few rows of the dataset:\")\n",
    "print(environmental_indicators.head())\n",
    "\n",
    "# Print the summary statistics of the dataset\n",
    "print(\"\\nSummary statistics of the dataset:\")\n",
    "print(environmental_indicators.describe())\n",
    "\n",
    "# Check for missing values in the dataset\n",
    "print(\"\\nMissing values in the dataset:\")\n",
    "print(environmental_indicators.isnull().sum())\n",
    "\n",
    "# Exclude non-numeric columns before computing the correlation matrix\n",
    "numeric_columns = environmental_indicators.select_dtypes(include=[np.number])\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7d892fee",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "Create a correlation matrix using the columns in the data. We do this to see whether there are potentially strong relationships between the independant variables and the dependant variable, but it can also be used to investigate potential multicollinearity between the independent variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d204bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Correlation matrix of the variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>forest_coverage</th>\n",
       "      <th>biodiversity_index</th>\n",
       "      <th>protected_areas</th>\n",
       "      <th>deforestation_rate</th>\n",
       "      <th>carbon_sequestration</th>\n",
       "      <th>soil_erosion</th>\n",
       "      <th>land_degradation</th>\n",
       "      <th>rural_population</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>forest_coverage</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274022</td>\n",
       "      <td>0.108666</td>\n",
       "      <td>0.556136</td>\n",
       "      <td>0.053950</td>\n",
       "      <td>-0.109275</td>\n",
       "      <td>0.154287</td>\n",
       "      <td>0.022843</td>\n",
       "      <td>0.027884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biodiversity_index</th>\n",
       "      <td>0.274022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067212</td>\n",
       "      <td>0.922463</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>-0.062205</td>\n",
       "      <td>-0.136184</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>-0.135042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protected_areas</th>\n",
       "      <td>0.108666</td>\n",
       "      <td>-0.067212</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120202</td>\n",
       "      <td>0.146215</td>\n",
       "      <td>0.321587</td>\n",
       "      <td>-0.113558</td>\n",
       "      <td>0.172193</td>\n",
       "      <td>-0.001360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deforestation_rate</th>\n",
       "      <td>0.556136</td>\n",
       "      <td>0.922463</td>\n",
       "      <td>0.120202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063443</td>\n",
       "      <td>-0.082024</td>\n",
       "      <td>-0.092885</td>\n",
       "      <td>0.105760</td>\n",
       "      <td>-0.126157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbon_sequestration</th>\n",
       "      <td>0.053950</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>0.146215</td>\n",
       "      <td>0.063443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.237919</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>-0.017830</td>\n",
       "      <td>-0.344947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soil_erosion</th>\n",
       "      <td>-0.109275</td>\n",
       "      <td>-0.062205</td>\n",
       "      <td>0.321587</td>\n",
       "      <td>-0.082024</td>\n",
       "      <td>0.237919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110673</td>\n",
       "      <td>0.146636</td>\n",
       "      <td>0.046528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_degradation</th>\n",
       "      <td>0.154287</td>\n",
       "      <td>-0.136184</td>\n",
       "      <td>-0.113558</td>\n",
       "      <td>-0.092885</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>-0.110673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085273</td>\n",
       "      <td>-0.137509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural_population</th>\n",
       "      <td>0.022843</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>0.172193</td>\n",
       "      <td>0.105760</td>\n",
       "      <td>-0.017830</td>\n",
       "      <td>0.146636</td>\n",
       "      <td>-0.085273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>0.027884</td>\n",
       "      <td>-0.135042</td>\n",
       "      <td>-0.001360</td>\n",
       "      <td>-0.126157</td>\n",
       "      <td>-0.344947</td>\n",
       "      <td>0.046528</td>\n",
       "      <td>-0.137509</td>\n",
       "      <td>0.226600</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      forest_coverage  biodiversity_index  protected_areas  \\\n",
       "forest_coverage              1.000000            0.274022         0.108666   \n",
       "biodiversity_index           0.274022            1.000000        -0.067212   \n",
       "protected_areas              0.108666           -0.067212         1.000000   \n",
       "deforestation_rate           0.556136            0.922463         0.120202   \n",
       "carbon_sequestration         0.053950            0.009418         0.146215   \n",
       "soil_erosion                -0.109275           -0.062205         0.321587   \n",
       "land_degradation             0.154287           -0.136184        -0.113558   \n",
       "rural_population             0.022843            0.042478         0.172193   \n",
       "population_density           0.027884           -0.135042        -0.001360   \n",
       "\n",
       "                      deforestation_rate  carbon_sequestration  soil_erosion  \\\n",
       "forest_coverage                 0.556136              0.053950     -0.109275   \n",
       "biodiversity_index              0.922463              0.009418     -0.062205   \n",
       "protected_areas                 0.120202              0.146215      0.321587   \n",
       "deforestation_rate              1.000000              0.063443     -0.082024   \n",
       "carbon_sequestration            0.063443              1.000000      0.237919   \n",
       "soil_erosion                   -0.082024              0.237919      1.000000   \n",
       "land_degradation               -0.092885              0.268428     -0.110673   \n",
       "rural_population                0.105760             -0.017830      0.146636   \n",
       "population_density             -0.126157             -0.344947      0.046528   \n",
       "\n",
       "                      land_degradation  rural_population  population_density  \n",
       "forest_coverage               0.154287          0.022843            0.027884  \n",
       "biodiversity_index           -0.136184          0.042478           -0.135042  \n",
       "protected_areas              -0.113558          0.172193           -0.001360  \n",
       "deforestation_rate           -0.092885          0.105760           -0.126157  \n",
       "carbon_sequestration          0.268428         -0.017830           -0.344947  \n",
       "soil_erosion                 -0.110673          0.146636            0.046528  \n",
       "land_degradation              1.000000         -0.085273           -0.137509  \n",
       "rural_population             -0.085273          1.000000            0.226600  \n",
       "population_density           -0.137509          0.226600            1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the correlation_matrix\n",
    "correlation_matrix = numeric_columns.corr()\n",
    "\n",
    "print(\"\\nCorrelation matrix of the variables:\")\n",
    "correlation_matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "20c19329",
   "metadata": {},
   "source": [
    "Looking at the matrix result, which variables appear to be related? \n",
    "\n",
    "It is, however, difficult to view the data well in table format. It might be more useful to display it as a heatmap.\n",
    "\n",
    "1) Use the `statsmodels.graphics.correlation` package to show the correlation heatmap for the variables. \n",
    "2) What relationships exist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "250d4a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exclude non-numeric columns (like 'country') and the dependent variable from the correlation matrix\n",
    "numeric_dependent_variables = environmental_indicators\n",
    "# Compute the correlation matrix\n",
    "corr_matrix = numeric_dependent_variables.corr()\n",
    "\n",
    "# Generate the correlation heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sgc.plot_corr(corr_matrix, xnames=corr_matrix.columns)\n",
    "plt.title('Correlation Heatmap of Variables')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d08d28aa",
   "metadata": {},
   "source": [
    "Based on the heatmap, we can see that there is some form of correlation between the following variables:\n",
    "1) `deforestation_rate` and `forest_coverage`\n",
    "2) `soil_erosion` and `protected_areas`\n",
    "3) `soil_erosion` and `carbon_sequestration`\n",
    "4) `land_degradation` and `carbon_sequestration`\n",
    "5) `deforestation_rate` and `biodiversity_index`\n",
    "6) `biodiversity_index` and `forest_coverage`\n",
    "\n",
    "Realistically, we can see that `forest_coverage` is related to `deforestation_rate` in some way, as there needs to be a forest before it can be deforested. `deforestation_rate` also has a relatively strong relationship with `biodiversity_index`.  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "90481009",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "Knowing what we know about the relationships between the variables, it's time to build our regression model. To get started, include all the numeric variables in the model. Remember that `biodiversity_index` is the dependent variable.\n",
    "\n",
    "Print the summary of the fitted model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d3401c",
   "metadata": {},
   "outputs": [],
   "source": [
    "independent_cols = ['forest_coverage',  'protected_areas',\n",
    "       'deforestation_rate', 'carbon_sequestration', 'soil_erosion',\n",
    "       'land_degradation', 'rural_population', 'population_density']\n",
    "\n",
    "dependent_var = 'biodiversity_index'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6477dc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating the regression string\n",
    "formula_str = dependent_var+ ' ~ ' + ' + '.join(independent_cols)\n",
    "formula_str\n",
    "# Construct and fit the model\n",
    "model = smf.ols(formula=formula_str, data=environmental_indicators)\n",
    "fitted = model.fit()\n",
    "\n",
    "# Print the summary of the fitted model\n",
    "print(fitted.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdb3a39",
   "metadata": {},
   "source": [
    "The output from this model seems somewhat strange, as there is a note warning of potential multicollinearity. In addition, some of the p-values on the coefficients are quite high (larger than we would like). It might be worth looking at which variables seem to be correlated with the dependent variable, keeping those, and maybe dropping a few other variables where there might be multicollinearity. The issue with this is that our model might be highly unstable. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e4bb6ae2",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "Create another version of the model with fewer independent variables and see if the warning can be silenced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "855c8fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropped population density, land_degradation, carbon sequestration\n",
    "independent_cols_reduced = ['forest_coverage',  'protected_areas',\n",
    "       'deforestation_rate', 'soil_erosion',\n",
    "        'rural_population']\n",
    "\n",
    "dependent_var = 'biodiversity_index'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b62622",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating the regression string\n",
    "formula_str = dependent_var+ ' ~ ' + ' + '.join(independent_cols_reduced)\n",
    "formula_str\n",
    "# Construct and fit the model\n",
    "model = smf.ols(formula=formula_str, data=environmental_indicators)\n",
    "fitted = model.fit()\n",
    "\n",
    "# Print the summary of the fitted model\n",
    "print(fitted.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2d4fae",
   "metadata": {},
   "source": [
    "This time, we can see that the warning about multicollinearity has disappeared, which is good news. The real test of our model will however be in it's predictive accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9080c93c",
   "metadata": {},
   "source": [
    "### Exercise 5\n",
    "\n",
    "Evaluate the accuracy and reliability of the multiple linear regression model by conducting various diagnostic tests.\n",
    "\n",
    "Check for linearity by visually inspecting scatter plots between each independent variable and the biodiversity index. This will help in understanding whether the relationship between the independent variables and the biodiversity index is linear.\n",
    "\n",
    "What can you see from the charts? Are there any linear relationships visible? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93e5c679",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the number of rows and columns for subplots\n",
    "num_cols = 2\n",
    "num_rows = (len(independent_cols_reduced) + num_cols - 1) // num_cols\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(num_rows, num_cols, figsize=(12, 8))\n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.3)\n",
    "axs = axs.ravel()\n",
    "\n",
    "# Iterate over each independent variable and create scatter plot against biodiversity index\n",
    "for index, column in enumerate(independent_cols_reduced):\n",
    "    axs[index].scatter(x=environmental_indicators[column], y=environmental_indicators['biodiversity_index'], color='blue', edgecolor='k')\n",
    "    axs[index].set_title(\"{} vs. Biodiversity_index\".format(column), fontsize=12)\n",
    "    axs[index].set_xlabel(column, fontsize=10)\n",
    "    axs[index].set_ylabel(\"Biodiversity_index\", fontsize=10)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8647390",
   "metadata": {},
   "source": [
    "The only variable that shows a relatively linear relationship with `biodiversity_index` is `deforestation_rate`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3508ef",
   "metadata": {},
   "source": [
    "### Exercise 6\n",
    "Visualise the relationship between predictor variables and model residuals to assess the independence of residuals using scatter plots. Do we see any obvious dependence of residuals? How would we know?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1369d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_matrix = environmental_indicators[independent_cols_reduced]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "008382f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting predictor variables vs. model residuals\n",
    "fig, axs = plt.subplots(2, 2, figsize=(14, 6), sharey=True)  # Adjusted to 2 rows and 4 columns\n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.2)\n",
    "fig.suptitle('Predictor variables vs. model residuals', fontsize=16)\n",
    "axs = axs.ravel()\n",
    "\n",
    "for index, column in enumerate(reduced_matrix):\n",
    "    if index < len(axs):  # Ensure we don't exceed the number of subplots\n",
    "        axs[index].set_title(\"{}\".format(column), fontsize=12)\n",
    "        axs[index].scatter(x=environmental_indicators[column], y=fitted.resid, color='blue', edgecolor='k')\n",
    "        axs[index].grid(True)\n",
    "        xmin = min(environmental_indicators[column])\n",
    "        xmax = max(environmental_indicators[column])\n",
    "        axs[index].hlines(y=0, xmin=xmin*0.9, xmax=xmax*1.1, color='red', linestyle='--', lw=3)\n",
    "        if index == 0 or index == 4:\n",
    "            axs[index].set_ylabel('Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e62681",
   "metadata": {},
   "source": [
    "In the charts above, it is clear that there are residuals present, but we cannot actually see any specific groupings or patterns emerging, which would suggest that the errors are independent. The loose distribution of errors around zero is a clear indication that external influences on the residuals are unlikely. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975ebaf3",
   "metadata": {},
   "source": [
    "### Exercise 7\n",
    "\n",
    "Check for homoscedasticity by conducting a `Breusch-Pagan` test on our model. Interpret the results to determine whether homoscedasticity could possibly be satisfied for our regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d73d971",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Calculate residuals\n",
    "residuals = fitted.resid\n",
    "\n",
    "# 2. Perform Breusch-Pagan Test\n",
    "bp_test_result = sms.het_breuschpagan(residuals, fitted.model.exog)\n",
    "print(\"Breusch-Pagan Test Results:\")\n",
    "print(\"LM Statistic:\", bp_test_result[0])\n",
    "print(\"LM-Test p-value:\", bp_test_result[1])\n",
    "print(\"F-Statistic:\", bp_test_result[2])\n",
    "print(\"F-Test p-value:\", bp_test_result[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82c275d",
   "metadata": {},
   "source": [
    "The p-values of both the LM test and the F-test are **larger than the typical significance level of 0.05**. This indicates that there isn't enough evidence to reject null hypothesis of homoscedasticity. This is good news for our model! \n",
    "\n",
    "In simpler terms, the variance of the residuals is constant across all values of the predictor variables, which means the model should be relatively reliable in predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b54a669",
   "metadata": {},
   "source": [
    "### Exercise 8\n",
    "Check for the normality of residuals by creating a Q-Q plot of the normalised residuals to visually assess their normality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd19232d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q-Q plot of normalised residuals\n",
    "plt.figure(figsize=(8, 5))\n",
    "fig = qqplot(fitted.resid_pearson, line='45', fit=True)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.xlabel(\"Theoretical quantiles\", fontsize=15)\n",
    "plt.ylabel(\"Sample quantiles\", fontsize=15)\n",
    "plt.title(\"Q-Q plot of normalised residuals\", fontsize=18)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2012af25",
   "metadata": {},
   "source": [
    "For the most part, our points lie close to the one-to-one line, which indicates that the residuals are normally distributed. There is however one rogue point on the plot that may warrant further investigation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3760aa4",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "\n",
    "To ensure the accuracy of our regression model, it's crucial to check for outliers that might significantly affect the model's coefficients and predictions. Use Cook's distance to visually inspect a plot of residuals vs. fitted values by identifying potential outliers. \n",
    "\n",
    "Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23159c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Cook's distance\n",
    "influence = OLSInfluence(fitted)\n",
    "(c, p) = influence.cooks_distance\n",
    "\n",
    "# Plot Cook's distance\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.title(\"Cook's distance plot for the residuals\", fontsize=16)\n",
    "plt.plot(np.arange(len(c)), c, marker='o', linestyle='-')\n",
    "plt.xlabel('Observation index')\n",
    "plt.ylabel(\"Cook's Distance\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58039a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "c"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "32ffe005",
   "metadata": {},
   "source": [
    "A rule of thumb for determining whether a Cook's distance is too large is whether it exceeds 1, or is greater than or equal to the value of 4 divided by the number of observations in the dataset. \n",
    "\n",
    "In our scenario, none of the distances exceed 1, and when we calculate 4 divided by the number of observations (32), we get **0.125**. \n",
    "\n",
    "There are three points that may demand more examination and influence our model. The values 0.23, 0.14, and 0.14 are all larger than the rule of thumb that can be used to spot outliers.\n",
    "\n",
    "Ideally, we should train the model again without those observations to observe how they influence it. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fd094b-0fee-46f1-a4b8-73766813c42b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#  \n",
    "\n",
    "<div align=\"center\" style=\" font-size: 80%; text-align: center; margin: 0 auto\">\n",
    "<img src=\"https://raw.githubusercontent.com/Explore-AI/Pictures/master/ExploreAI_logos/EAI_Blue_Dark.png\"  style=\"width:200px\";/>\n",
    "</div>"
   ]
  }
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