From 5a8c6a20cdaf5efa3abf222096253db030878659 Mon Sep 17 00:00:00 2001
From: SHARVAN JEET <imsharvanj@gmail.com>
Date: Fri, 27 Oct 2017 01:01:32 +0530
Subject: [PATCH 1/3] Update scrpit.js

Added color to remaining blocks
---
 static/script.js | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/static/script.js b/static/script.js
index 3583ed7..b1e0c9d 100644
--- a/static/script.js
+++ b/static/script.js
@@ -28,18 +28,28 @@ else{
                  switch(mycolor){
                  case "2": v.rows[i].cells[j].style.backgroundColor="#EEE4DA";
                        break;
-                 case "4":v.rows[i].cells[j].style.backgroundColor="#EDE0C8"
+                 case "4":v.rows[i].cells[j].style.backgroundColor="#EDE0C8";
                  break;
-                 case "8":v.rows[i].cells[j].style.backgroundColor="#F2B179"
+                 case "8":v.rows[i].cells[j].style.backgroundColor="#F2B179";
                  break;
-                 case "16":v.rows[i].cells[j].style.backgroundColor="#F59563"
+                 case "16":v.rows[i].cells[j].style.backgroundColor="#F59563";
                  break;
-                 case "32":v.rows[i].cells[j].style.backgroundColor="#F67C5F"
+                 case "32":v.rows[i].cells[j].style.backgroundColor="#F67C5F";
+                 break;
+                 case "64":v.rows[i].cells[j].style.backgroundColor="#F65E3B";
+                 break;
+		 case "128":v.rows[i].cells[j].style.backgroundColor="#EDCF72";
+                 break;
+		 case "256":v.rows[i].cells[j].style.backgroundColor="#EDCC61";
+                 break;
+		 case "512":v.rows[i].cells[j].style.backgroundColor="#EDC850";
+                 break;
+		 case "1024":v.rows[i].cells[j].style.backgroundColor="#EDC470";
+                 break;
+		 case "2048":v.rows[i].cells[j].style.backgroundColor="#EDA157";
                  break;
-                 case "64":v.rows[i].cells[j].style.backgroundColor="#F65E3B"
-                 break
                  default:
-                 v.rows[i].cells[j].style.backgroundColor="#CDC1B4"
+                 v.rows[i].cells[j].style.backgroundColor="#CDC1B4";
                  
 
 

From 5be2a8e61a6e91fee9b9e46006d37db732547044 Mon Sep 17 00:00:00 2001
From: SHARVAN JEET <imsharvanj@gmail.com>
Date: Sat, 16 Jul 2022 18:36:20 +0530
Subject: [PATCH 2/3] Created using Colaboratory

---
 shreya_flfpr_gdp.ipynb | 1502 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1502 insertions(+)
 create mode 100644 shreya_flfpr_gdp.ipynb

diff --git a/shreya_flfpr_gdp.ipynb b/shreya_flfpr_gdp.ipynb
new file mode 100644
index 0000000..c15abba
--- /dev/null
+++ b/shreya_flfpr_gdp.ipynb
@@ -0,0 +1,1502 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "shreya_flfpr_gdp.ipynb",
+      "provenance": [],
+      "authorship_tag": "ABX9TyOfuEjUih0SdYqCGQgPcV6A",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/imsharvanj/2048/blob/master/shreya_flfpr_gdp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fJbayRGGokUm",
+        "outputId": "39901c45-64a9-4de6-eba4-b97afde4c566"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/gdrive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/gdrive', force_remount=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import r2_score\n",
+        "import scipy.stats as stats"
+      ],
+      "metadata": {
+        "id": "FaZsv8-lwupx"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('gdrive/My Drive/_python notebooks/data.csv')\n",
+        "df.rename(columns={'GDP per capita': 'gdp_per_capita', 'Year': 'year'}, inplace=True)\n",
+        "df.name = 'Cummulative Data'\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Eee-kd3xpOD0",
+        "outputId": "cab8b1c3-e6e8-46cc-8f13-34850f3101ec"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   year  gdp_per_capita      flfpr\n",
+              "0  1990      367.556609  30.443001\n",
+              "1  1991      303.055608  30.452999\n",
+              "2  1992      316.953927  30.493000\n",
+              "3  1993      301.159002  30.570000\n",
+              "4  1994      346.102951  30.691999"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c804290d-949b-471f-8dc1-9f524b0314b0\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>flfpr</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1990</td>\n",
+              "      <td>367.556609</td>\n",
+              "      <td>30.443001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1991</td>\n",
+              "      <td>303.055608</td>\n",
+              "      <td>30.452999</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>316.953927</td>\n",
+              "      <td>30.493000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1993</td>\n",
+              "      <td>301.159002</td>\n",
+              "      <td>30.570000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1994</td>\n",
+              "      <td>346.102951</td>\n",
+              "      <td>30.691999</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c804290d-949b-471f-8dc1-9f524b0314b0')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-c804290d-949b-471f-8dc1-9f524b0314b0 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-c804290d-949b-471f-8dc1-9f524b0314b0');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['log_gdp_per_capita'] = np.log2(df['gdp_per_capita'])\n",
+        "df['log_gdp_per_capita_squared'] = df['log_gdp_per_capita']**2\n",
+        "df['abs_flfp'] = df['flfpr']/100\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "fvGBL89fpP7O",
+        "outputId": "f5774197-ce81-45ae-e8fd-6d2c24fba5ea"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
+              "0  1990      367.556609  30.443001            8.521823   \n",
+              "1  1991      303.055608  30.452999            8.243439   \n",
+              "2  1992      316.953927  30.493000            8.308129   \n",
+              "3  1993      301.159002  30.570000            8.234382   \n",
+              "4  1994      346.102951  30.691999            8.435057   \n",
+              "\n",
+              "   log_gdp_per_capita_squared  abs_flfp  \n",
+              "0                   72.621461   0.30443  \n",
+              "1                   67.954282   0.30453  \n",
+              "2                   69.025013   0.30493  \n",
+              "3                   67.805040   0.30570  \n",
+              "4                   71.150194   0.30692  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dc682b88-734f-451e-a8dd-81282f2ef209\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>flfpr</th>\n",
+              "      <th>log_gdp_per_capita</th>\n",
+              "      <th>log_gdp_per_capita_squared</th>\n",
+              "      <th>abs_flfp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1990</td>\n",
+              "      <td>367.556609</td>\n",
+              "      <td>30.443001</td>\n",
+              "      <td>8.521823</td>\n",
+              "      <td>72.621461</td>\n",
+              "      <td>0.30443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1991</td>\n",
+              "      <td>303.055608</td>\n",
+              "      <td>30.452999</td>\n",
+              "      <td>8.243439</td>\n",
+              "      <td>67.954282</td>\n",
+              "      <td>0.30453</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>316.953927</td>\n",
+              "      <td>30.493000</td>\n",
+              "      <td>8.308129</td>\n",
+              "      <td>69.025013</td>\n",
+              "      <td>0.30493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1993</td>\n",
+              "      <td>301.159002</td>\n",
+              "      <td>30.570000</td>\n",
+              "      <td>8.234382</td>\n",
+              "      <td>67.805040</td>\n",
+              "      <td>0.30570</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1994</td>\n",
+              "      <td>346.102951</td>\n",
+              "      <td>30.691999</td>\n",
+              "      <td>8.435057</td>\n",
+              "      <td>71.150194</td>\n",
+              "      <td>0.30692</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc682b88-734f-451e-a8dd-81282f2ef209')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-dc682b88-734f-451e-a8dd-81282f2ef209 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-dc682b88-734f-451e-a8dd-81282f2ef209');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Recession happened between in 2006-2009. So we are dividing data in 2 parts.**\n"
+      ],
+      "metadata": {
+        "id": "n5OUv123V8Nj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# before 2006\n",
+        "df_before_2006 = df[df['year'] < 2006]\n",
+        "df_before_2006.name = 'Before 2006'\n",
+        "df_before_2006.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "WWN_yLusQ5oi",
+        "outputId": "30bd9540-2fc0-42b2-bda6-053a77c8589a"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
+              "0  1990      367.556609  30.443001            8.521823   \n",
+              "1  1991      303.055608  30.452999            8.243439   \n",
+              "2  1992      316.953927  30.493000            8.308129   \n",
+              "3  1993      301.159002  30.570000            8.234382   \n",
+              "4  1994      346.102951  30.691999            8.435057   \n",
+              "\n",
+              "   log_gdp_per_capita_squared  abs_flfp  \n",
+              "0                   72.621461   0.30443  \n",
+              "1                   67.954282   0.30453  \n",
+              "2                   69.025013   0.30493  \n",
+              "3                   67.805040   0.30570  \n",
+              "4                   71.150194   0.30692  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a47d2933-639e-497e-8977-826a689f980b\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "\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>year</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>flfpr</th>\n",
+              "      <th>log_gdp_per_capita</th>\n",
+              "      <th>log_gdp_per_capita_squared</th>\n",
+              "      <th>abs_flfp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1990</td>\n",
+              "      <td>367.556609</td>\n",
+              "      <td>30.443001</td>\n",
+              "      <td>8.521823</td>\n",
+              "      <td>72.621461</td>\n",
+              "      <td>0.30443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1991</td>\n",
+              "      <td>303.055608</td>\n",
+              "      <td>30.452999</td>\n",
+              "      <td>8.243439</td>\n",
+              "      <td>67.954282</td>\n",
+              "      <td>0.30453</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>316.953927</td>\n",
+              "      <td>30.493000</td>\n",
+              "      <td>8.308129</td>\n",
+              "      <td>69.025013</td>\n",
+              "      <td>0.30493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1993</td>\n",
+              "      <td>301.159002</td>\n",
+              "      <td>30.570000</td>\n",
+              "      <td>8.234382</td>\n",
+              "      <td>67.805040</td>\n",
+              "      <td>0.30570</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1994</td>\n",
+              "      <td>346.102951</td>\n",
+              "      <td>30.691999</td>\n",
+              "      <td>8.435057</td>\n",
+              "      <td>71.150194</td>\n",
+              "      <td>0.30692</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a47d2933-639e-497e-8977-826a689f980b')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
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+              "\n",
+              "    .colab-df-convert {\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-a47d2933-639e-497e-8977-826a689f980b button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-a47d2933-639e-497e-8977-826a689f980b');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# after 2006\n",
+        "df_after_2006 = df[df['year'] >= 2006]\n",
+        "df_after_2006.name = 'After 2006'\n",
+        "df_after_2006.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "BQIDOa43RMNY",
+        "outputId": "59ee221d-ee93-4d23-a4b5-8dfc63920bc0"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
+              "16  2006      806.753281  30.712000            9.655984   \n",
+              "17  2007     1028.334772  29.490000           10.006094   \n",
+              "18  2008      998.522342  28.290001            9.963651   \n",
+              "19  2009     1101.960838  27.114000           10.105857   \n",
+              "20  2010     1357.563727  25.965000           10.406804   \n",
+              "\n",
+              "    log_gdp_per_capita_squared  abs_flfp  \n",
+              "16                   93.238022   0.30712  \n",
+              "17                  100.121923   0.29490  \n",
+              "18                   99.274339   0.28290  \n",
+              "19                  102.128351   0.27114  \n",
+              "20                  108.301574   0.25965  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ec9a31e7-84eb-480a-8f34-cd319c38cade\">\n",
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+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>flfpr</th>\n",
+              "      <th>log_gdp_per_capita</th>\n",
+              "      <th>log_gdp_per_capita_squared</th>\n",
+              "      <th>abs_flfp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>2006</td>\n",
+              "      <td>806.753281</td>\n",
+              "      <td>30.712000</td>\n",
+              "      <td>9.655984</td>\n",
+              "      <td>93.238022</td>\n",
+              "      <td>0.30712</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>2007</td>\n",
+              "      <td>1028.334772</td>\n",
+              "      <td>29.490000</td>\n",
+              "      <td>10.006094</td>\n",
+              "      <td>100.121923</td>\n",
+              "      <td>0.29490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>2008</td>\n",
+              "      <td>998.522342</td>\n",
+              "      <td>28.290001</td>\n",
+              "      <td>9.963651</td>\n",
+              "      <td>99.274339</td>\n",
+              "      <td>0.28290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>2009</td>\n",
+              "      <td>1101.960838</td>\n",
+              "      <td>27.114000</td>\n",
+              "      <td>10.105857</td>\n",
+              "      <td>102.128351</td>\n",
+              "      <td>0.27114</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>2010</td>\n",
+              "      <td>1357.563727</td>\n",
+              "      <td>25.965000</td>\n",
+              "      <td>10.406804</td>\n",
+              "      <td>108.301574</td>\n",
+              "      <td>0.25965</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec9a31e7-84eb-480a-8f34-cd319c38cade')\"\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-ec9a31e7-84eb-480a-8f34-cd319c38cade button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-ec9a31e7-84eb-480a-8f34-cd319c38cade');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
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+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def draw_scatter_plot(df): \n",
+        "  sns.scatterplot(x='log_gdp_per_capita',y='abs_flfp',data=df) \n",
+        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
+        "  plt.ylabel('FLFP')\n",
+        "  plt.xlabel('log(GDP per capita)')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "gD5_B5qStmMH"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def draw_reg_plot(df):\n",
+        "  sns.regplot(x='log_gdp_per_capita',y='abs_flfp',data=df, order=2) \n",
+        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
+        "  plt.ylabel('FLFP')\n",
+        "  plt.xlabel('log(GDP per capita)')\n",
+        "  plt.show()"
+      ],
+      "metadata": {
+        "id": "Jw3wmMfWSGIz"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def draw_extended_reg_plot(df):\n",
+        "  # degree 2 polynomial fit or quadratic fit\n",
+        "  model = np.poly1d(np.polyfit(df['log_gdp_per_capita'],\n",
+        "                              df['abs_flfp'], 2))\n",
+        "    \n",
+        "  # polynomial line visualization\n",
+        "  polyline = np.linspace(0, 20, 100)\n",
+        "  plt.scatter(df['log_gdp_per_capita'], df['abs_flfp'])\n",
+        "  plt.plot(polyline, model(polyline))\n",
+        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
+        "  plt.ylabel('FLFP')\n",
+        "  plt.xlabel('log(GDP per capita)')\n",
+        "  plt.show()\n",
+        "  return model\n",
+        "  "
+      ],
+      "metadata": {
+        "id": "jTJvdOCqyyQV"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_r2_score(df, model):\n",
+        "  return r2_score(df['abs_flfp'], \n",
+        "               model(df['log_gdp_per_capita']))"
+      ],
+      "metadata": {
+        "id": "axwdZ6VvXb-Z"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_coorelation(df):\n",
+        "  return abs(df['log_gdp_per_capita'].corr(df['abs_flfp']))"
+      ],
+      "metadata": {
+        "id": "5WN8pN-DEWQY"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **BEFORE 2006**"
+      ],
+      "metadata": {
+        "id": "cPLc-wrrYNkm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_scatter_plot(df_before_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "Txldtlu6YWUA",
+        "outputId": "3fce28bc-2822-4289-c0d1-e7f0729e76c3"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_reg_plot(df_before_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "77SgC4_LUszN",
+        "outputId": "2b7ce4c6-577d-4df4-c411-ca5bd34385b3"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_before_2006 = draw_extended_reg_plot(df_before_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "8FHEfW7XVeOH",
+        "outputId": "ad90e8c3-2f32-4639-de44-e966bfe88108"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(model_before_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sF1KDJ0zYjuu",
+        "outputId": "6a4f930d-c40c-4941-f83e-659f5ce8c0ac"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "         2\n",
+            "0.01261 x - 0.211 x + 1.188\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_r2_score(df_before_2006, model_before_2006))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YQZ9KOp8Ytw8",
+        "outputId": "7775e97f-fa34-44c2-be07-75ad9192f22e"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9093691704816828\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_coorelation(df_before_2006))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V0WtDWp-FO5e",
+        "outputId": "82ff6640-ef15-462a-df63-9e04b0a6d52d"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8674146211561067\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> **Regression result before 2006**\n",
+        "\n",
+        "| Variable      | Coefficient |\n",
+        "| ----------- | ----------- |\n",
+        "| (log GDP per capita)^2      | ß0 = 0.1261       |\n",
+        "| log GDP per capita   | ß1 = -0.211        |\n",
+        "| R-squared      | 0.9093691704816828       |\n",
+        "| α   | 1.188        |\n",
+        "\n",
+        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
+        "\n",
+        "\n",
+        "> **Coorelation result before 2006**\n",
+        "\n",
+        "Coorelation between FLFP and log GDP per capita: **0.8674146211561067**\n",
+        "\n",
+        "*This shows strong coorelation*"
+      ],
+      "metadata": {
+        "id": "wKp06W9lfyjt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **AFTER 2006**"
+      ],
+      "metadata": {
+        "id": "aobre6FYY4H2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_scatter_plot(df_after_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "lD4XYeHvZUGJ",
+        "outputId": "afeb66c6-bdd5-4ae4-9366-21d0a2bae0ff"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_reg_plot(df_after_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "WGNMSPEPWtU6",
+        "outputId": "780da54a-7ef3-49d9-dc55-ee006faee247"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_after_2006 = draw_extended_reg_plot(df_after_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "4OQFu29lWz0T",
+        "outputId": "f49b4270-e68c-434b-c304-83d2a85587f1"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(model_after_2006)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7J8nOHnQAbt3",
+        "outputId": "6ae37af1-27c6-483c-8bd2-aeb0c7338ce1"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "        2\n",
+            "0.0137 x - 0.3686 x + 2.596\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_r2_score(df_after_2006, model_after_2006))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-6-r63AIXswO",
+        "outputId": "7c34cee2-aec7-4bd4-fbf0-a544fafd8ebd"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9298091200395427\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_coorelation(df_after_2006))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ACIjE-vNGX0r",
+        "outputId": "ad9e5325-2195-4f5a-e5bb-704b19a46f82"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9617973838637672\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> **Regression result after 2006**\n",
+        "\n",
+        "| Variable      | Coefficient |\n",
+        "| ----------- | ----------- |\n",
+        "| (log GDP per capita)^2      | ß0 = 0.0137       |\n",
+        "| log GDP per capita   | ß1 = -0.3686        |\n",
+        "| R-squared      | 0.9298091200395427       |\n",
+        "| α   | 2.596        |\n",
+        "\n",
+        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
+        "\n",
+        "\n",
+        "> **Coorelation result after 2006**\n",
+        "\n",
+        "Coorelation between FLFP and log GDP per capita: **0.9617973838637672**\n",
+        "\n",
+        "*This shows strong coorelation*"
+      ],
+      "metadata": {
+        "id": "5F-3vmjWg6ZQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **CUMMULATIVE**"
+      ],
+      "metadata": {
+        "id": "qsabvQSrZmr2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_scatter_plot(df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "RMyEgx93ZsIb",
+        "outputId": "f93f195a-04f7-4b75-cf99-29df99d3c7ed"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "draw_reg_plot(df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "z4xOmx13ZzOr",
+        "outputId": "62429a1f-700c-4c6d-d78d-501e2835c4dd"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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X+LAMzJ2MYHsInoDtwqIOp4M2xnwM2wtsMujX9zLO/SNwsojMTEm7kqM+nr7r3Nvp2B50J2JbBPeHh4C3p+w/iv0MarGtxn/t1PVeZ/t7Tl2Tbjx2OOeXY1sP/0pEBupFQTnBUIGhDAeqgUNDYMH6Y2NMozFmH/A34B/GmFeMMWFsYZQZK/obxpiw416hE/iNMaYp5fx+x5Y2xmw3xvzVGBMxxhwEfkC6f6Tezu3CdkmxDOzwpNhuuleJiGAHz/m8MSbpkfjbwBX9rOJ+bL9RyWv+zBjTboyJYAu2+c6Ir6c6/s4Ys98YYxljHsAOFLS4n3VQTlBUYCjDgWZs19+DNSTNdFGf02X9IPL3ieTnRro3enI5XoPtrPAlOepa+y9Oen+YiG1tjQzA1br07XpfGcGowFCGAy9gO6F7Xy958nHNXij6c+2+3Ej3ZSnbk8vxQ9hCbG6KW+1yR6ndH96PPXqCfrpal/xc7ysjGBUYynHHGNOKPRd/j4i8T2z3zV4ReY+IJOf5C+KaPU/WA+c4NhPl2FHTeqJXN9L04b68J5fjjnv1nwJ3ikgtgNiuvC/oq/LOSGKaiPwYO0Ld11Pq2h9X6/m43ldGMCowlGGBMeb72DE0buKoW/PrsIP3QGFds/dVt78619uA7fL6T71k78uN9Hew41kcEZEv9lBGTy7Hv4Lt2nqNM4X0BHaQqJ5YIrbb7zbssLFlwCJjzGvO8X65Ws/T9b4yglFfUoqiKEpe6AhDURRFyQsVGIqiKEpeqMBQFEVR8kIFhqIoipIXIybi3pgxY8zUqVOPdzUURVFOKF566aVDxpi8DEBHjMCYOnUq69at6zujoiiK0o2I7M43r05JKYqiKHmhAkNRFEXJCxUYiqIoSl6owFAURVHyQgWGoiiKkhcFFRgicqGIvCEi20XkxhzHPy0irzn+9f8uInOc9PNF5CXn2Esicm4h66koq7c0sezeNZx921Msu3cNq7c0He8qKcqwo2ACQ0TcwD3Ae4A5wLKkQEjhfmPMacaY04HvYUcnA9v3/8XGmNOAT2B7KlWUgrB6SxM3r9pIU3uYiqCXpvYwN6/aqEJDUTIo5AhjMbDdGLPTGBMFVmIHa+nGGNOWspv0tY8TVnO/k74RCIqIv4B1VUYxK57didctFPk8iNj/vW5hxbM7j3fVFGVYUUjDvYnYMQ2S7AXOzMwkIp/FjoPgA3JNPX0QeNmJOZx57jXYcY6ZPHnyEFRZGY3Ut3RREfSmpQW9bva2dB2nGinK8OS4K72NMfcYY07CDg5zU+oxEZkL3AZc28O59xpjFhpjFtbU9De0sXIiM5Q6h7rKIkKxRFpaKJZgUmVRD2coyuikkAJjH1CXsj/JSeuJlaTEdBaRScDvgY8bY3YUpIbKCclQ6xyuPWc6sYShKxrHGPt/LGG49pweI6kqyqikkAJjLTDTiSXsA64AVqVmEJGZKbv/BGxz0iuww1veaIzREJBKGkOtc1g6u5bll8yltjRAayhGbWmA5ZfMZens2iGuuaKc2BRMh2GMiYvIdcBjgBv4mTFmo4gsB9YZY1YB14nIeUAMaMFeEQV2LOcZwM0icrOT9m5jjC5bOQ4YY7AMWMZgGYMxkBrZV+Tof5cILhHcLilYfQqhc1g6u1YFhKL0QUG91RpjHgEeyUi7OWX7+h7O+ybwzULWbTRjjCFuGeIJQ8yyiCcMccvCOEIhYZnubcvY+QeC22ULj3/sbOb+F/fQ0BpmQnmQTyyZwjkn1+ByCR6XLVy8LheuPIVMXWURTe1hinxHm6/qHBSl8IwY9+ZKOrGERcIyxBJJgWALheT2QIVAf0hYhhd2NvPDp7bhcQklfjdN7WG+85ctdEUTLJ5elZbf7RI8bhfe5H+34PO48LldiBwVJteeM52bV22kKxon6HUTiiVU56AoxwAVGCcwSYEQTVjE4haxhCMgjpFAyIeVa+vxuISg1w3Q3cGvXFufJTASliFhJchcPy0ieN2C3+PG73Vx1owxLL9kLiue3cneli4mVRZx7TnTdUpJUQqMCowTBGMMkbjl/CWIxCxiCWtIr/HizsOsXFtPQ1uI8WVBrlhUl9Wp95eGthBlgfRmFvC6ONAWyrsMYwzRuCEat2gP22nTaoq58/LTCXhdBLxu/J7jvkJcUUY8KjCGKbGERTiWIBK3/8cS+Y0aLGM40hXjcGeUlq4oLZ1RWkMx2sJx2sNxOiJxuqJxuqIJwrEEUUcIdUbsY0kOtkd4dd8RirxuAl43Hrfgdbvwe+wOOuB1U+xzU+z3UOx3UxbwUh60/yqLfFSV+Kgq9jG+LEhzZ6R7hAEQjlmMKwsO6vkkLOPch70vIgS8LoJO3QIp11MUZWhQgTFMiCUsQjG7Ew9HLeJW7tGDMYbmzij7WkLsOxKioTXMgdYwB9rCHGyP0NwZJWEN3XRUZzRBZzTRd8YeCHhdxBMGj0vwe+1RgEuEDy6YRCSWwD9EHbsxhlA0Qcipq0uEgNdtCxCfC79HBYiiDBYZLnPdg2XhwoXmRIrp3S0gognCsWwBYYzhUEeUHQc72Hmwk92Hu9jT3MXuw52EY/lNRbldQkWRl4qgl7Kgl9KAhxKfhyK/myKvh4DXhc9jT+f89G87KfK5EY4qlw2GUCzBZ985o1s/khzxhGMJuiIJOpyRSXvYHsUc6YqljVR6Q4DaMj+Tq4qYWl3MtDHFTK8pZmp1Mb4hnmLyuFwEfC6KfB6KvO68V2QpykhHRF4yxizMJ6+OMI4RcUdAhGLZ+gdjDAfbI2w+0M7Wxna2NnawrbGdtnDvHW9NiZ8JFQHGlQcYWxqgtsxPTamfMSV+qop9lAY8uCS7Y8ylq5g+piRr6igUSzCuLMh5p4zt171G4xaHu6Ic7ohysCNCU1uYxvYIja1hGlrD7D8SIhy3MEBjW4TGtghrd7V0n+8SmFJdzIzaEk4eW8rscaXMqC0ZlBCJWxYdYYuOcNwx9rOn01R4KEr+6AijAKze0sRPntnBnpYuJpQHuXxhHW+ZWtl9PJ6w2NbUwWv7Wtm4v41NDW00d0RzluV2CVOqiphSbX+FT64uoq4yyISK4IDm6V/cebh7mWvA63JGN4YL54zlL5sas9KvP3fmoBXfmSSn1eoPd1HfEmJPcxdvNney61AnLV2xnOd4XMKM2hLmTCjj1AllzJtUQVWxb9B1GWrhsXpLEyue3Ul9Sxd1unpLOQHozwhDBcYQkbAM4ViCJzc38t1Ht+BO6XhjCYsPLJhENGHxyp4WNu5vIxzPnlYSgcmVRZwxpZJZY0uYUVPC5OoivG5XjyuYUtMnlAe5cvFklsyoRkS6leRJy2yD4fqV62nuyB5JVBf7uWJRHSvX1nOgLcS4IVol1V8Od9rTcNsaO9jW1MGWA200tmU5KgZgUmWQeZPKWVBXyYLJgxcgItKtyC/yudNsP/Ih6ePK65Y0+xB1M6IMZ1Rg9IOBfhEmLHt+P+SsNkpOMd3wwKs0d0bwuITOSIKuWJzOSIJcT7mq2EcoGifgdVPqd2MZSBiyvuqTo4JkRxSJ2wZ4CyaX89jGRhKWscsIePB53FkdVOo9HmyPMK7MT2ngqGsNyxjaQjEev+EdJCyDZdlTOAnLNvJL/j9ebeVIV5TNDe1samhj4/5WNje0E8khcKePKWbh1EoWT63i1Inlg5rCcruEEr+H0oA373KW3bsmywK9KxqntjTAb65564DroiiFRHUYeZL8IowlErR2xWhoDfHynhY+u/QkPnferLSOdmJFkKuWTGXR9Kru5aipJCzD6/ta2drUTjxhEU1kd66TKoMsqKtgfl0FVsLww6e2dXd8RT43xT5PmlGb1+0i6HPz0Cv7CHpdFPvtTt7ncXOwPcyfNhzoXu4aT9jTPNXFPlY8u7NbYCTvMRpP0B6OE41b7D4cYmypRW1ZAIBILEFdVXFaR5eLRIa1eNwxEozGC2ssWFHkY8lJ1Sw5qRqwFwy8caCdDXtbWV9/hNf2tRKJW+w81MnOQ538dt1eAh4XZ0ypZMn0at46vYrqkv7F30pYhtZQjNZQjKDPTWnAS3Efow6Nq6GMdEa1wFjx7E5iiQTNHTFEwOt2kbAMdz+9nVjC4qFX9uNx2T/6/UdCLP/zprSv/0gswdpdLTy34xAv7GjOUlK7BIp9HjxuYXxZkLs/sgA4OmIIxxK4XRC3DE1tEWrLoMTv4WB7mLoqeyoKYH9rKKsjag/HMdhfwoLYDgAtOz21g1rx7E6i8QTNnVFcCB4XxCxobI8Q8LrwuF15u9VwuwS3y42/h1YTT9jW5tGEPQ0XS1hE49aQLvMF+z2dOrGcUyeWc+WZk4nGLTY3tLFudwvrdrXwRmM74bjF8zuaeX5HMwCnjC/l7BljOHvGGOqq+udzKrlc1+0SSgP2arPku0lFfVwpI51RLTDqW7po7YoBxl5Oamx/73HL8LPndlFV7Otev5+ck77/xT2EEwmeeeMga3Yezgq8M6E8QFs4RpHPQ1nATSRuf41/fMmU7jxJdxk+j4tEwuByCRaGls4oAa+bydXFaR1Sro4oErcQbN1EqrfYSNxK66DqW7poD8dxIbhcggs3kCBmwYG2CGdMrhwyxazH7cLjhiDpyvjkctxILEHY+T+U+Dwu5jsjt6vPnkZLV5S1u1pYs6OZtbsO0xlNsLmhnc0N7fz0b28yfUwx7zi5hqWzavolPBKW4UhXlCNdUYp8HkoDHopTpKf6uFJGOqNaYNRVFtHQGsLjkm4dg2XsDigUSxBwDM3soDoJ2iJx9raE2LCvtbsMl8DpdRW8bcYYzjqpmrFlgW5FdE/K4wNtISqKvAh+GtrC3Z1+xPlCz+xgcnVEbpdQFvDQEUlgYRCBhDG4XZJ2fl1lEQdaw3hSVv+4XC6K3FBd4j8mc+tetwuv20WJ07laljnq4iRuEclhhzIYKot8vHvOWN49ZyzxhMWre1v5+7ZD/H3HIZo7ot1TV//z3C5m1JZw3im1vPPkWmpK85+2sq3M43hcLsqCtq5j6exaloP6uFJGLKNa6b16SxPX/uolEpaFS6R7JVFF0EsoZlHi9xCNW7RF4mnTKi6Bt0yp5B2zanjbSWMoL/L2chUbr9vV7Ubjqp+t7R4xtIViHOqIEIlbFPnc/OiKBTk7mKQ+JdkRLZlexYMv7+vWv0QSFh6Xq1v/knmPliNMkiumqku8TK0uGTbK2GjcIhzv29J9MFjGsLmhjWe2HmT1Gwc5lLKUWYAFkyu4YO44zp45Jm0VWT6I2Ery8mD+SnJFGQ7oKql+8KMntnL309tJWAafx0XQ6yJu2R18c2e6bYTPLVx06ng+ftYUKor6XsIpIhT73ZT6vQR9RzugoVp+mSlEevqa/dETW7ln9Q7iloXf7aK8yIvXnb2aajiRFCBJ/YE1xO3UMoaN+9p4cksTq99oStM/Bb1u3nlyDRedNp5Txpf2e3ltkc8WHKnvXFGGK8NGYIjIhcAPsSPu/bcx5rsZxz8NfBZIAB3ANcaYTc6xrwJXO8c+Z4x5rLdrDcYO45EN+7nzr9uoP9JFJGalLYH1e+y4DHWVRXxiydS87BK8bhdlAS8lAU+Pkefy7eyHimN9vaEm7CxhDjkOGYey3cYSFmt3HebxTY28sKOZWMoKt2ljirnotHFcMGccJYH+zeD6vW4qgt40PYeiDDeGhcAQETewFTgf2Isd43tZUiA4ecqMMW3O9iXAZ4wxF4rIHOA3wGJgAvAEMMsY06O2dKAC4w/r93H3U9vZ1tTRnRbwunjnybW859RxzJ1QlvcXpn5ZHhssxwamyxl9DOX0VVsoxlNbmnjktQNsP3i0Tfg9Ls6dXcsl8ydw8rjSfpXpdbuoLPZ163AUZTgxXOwwFgPbjTE7nUqtBC4FugVGUlg4FEP3x/2lwEpjTAR4U0S2O+W9MNSV3NsS6hYWs8aW8N55Ezh3dk2fNgmpFPk8VBR51aX2McLlEkcfZL+jSNx2hNgVSwx6BVZZ0Mv7Fkzk0tMnsK2pgz9taOCJzY2EYxaPvn6AR18/wJzxZbx/wUTOmTUm5/LaTGIJi6a2MC1uF1XFPh1xKCcshWy5E4H6lP29wJmZmUTks8ANgA84N+XcNRnnTsxx7jXANQCTJ08eUBPD8MMAACAASURBVCUvWziJNw91ct4ptcwam/+XY9KNRHmRV11nH2f8Hjd+j5tKbFuQzmiCrmi829X5QBARZo0t5YbzS7n2nOk8sbmRVa828OahTjY12P6/fvKsj/efPpH3zhtPWbDvhQ+xhEVjWxi/1011sU8/MJQTjkJOSX0IuNAY8yln/2PAmcaY63rIfyVwgTHmEyJyN7DGGPMr59h9wKPGmAd7ut5gdBgdkThNbeG88ooIpQEPFUEvnjy+LpXjR8IydEbjdEYGJzySGGN4dW8rv39lH89tP0Ry4VzA4+KCU8fx4YWTGF+ef2CoIp+HymL94FCOL8NlSmofUJeyP8lJ64mVwH8N8NxjQknAQ2WRL69pCOX4Y9uqeCkLeElYho6ILTzCA5y2EhFOr6vg9LoKDrSF+f3L+/jzaw10RRP8Yf1+/vjqfpaeXMsVi+qYUVvSZ3lJW46SgIeqIp9+gCjDnkKOMDzYSu93YXf2a4ErjTEbU/LMNMZsc7YvBm4xxiwUkbnA/RxVej8JzCyE0hv6HmEEfe40q2/lxCaWsONidETig46L3hmJ88hrDTz40j4Odhz1qvvW6VV89MwpzJlQllc5LrGDXZUHvf1extsb6m5d6YthsUrKqchFwF3Yy2p/Zoz5logsB9YZY1aJyA+B84AY0AJclxQoIvI14J+BOPDvxphHe7tWIQSGV5WUI55wzHbK2BmJD8rWI5aweGpLEyvX1rO7+agvrzMmV/DxJVOYN6kir3K8bhfVJb5+LbroCXW3ruTDsBEYx5KhFBh2aFMfZQHPkH7tKcMXyzJ0ROO0h+ODWmllGcNz25v59T92s7Xx6LLcMyZX8IklUzltUnle5RT5PFSXDG76U92tK/kwXHQYJxwitn+myiKfhu0cZbhS9B0RxxV8R7j/ow6XCG+fOYazZ1SzdlcLv3hhF5sa2nl5zxFe3rOeRVMr+ee3TevTlqMrGifUkqAi6LX9jg3gw0XdrStDjQoMhyKfh6pin/oBUuxluiVuqop8dETjtHbF+q3rEBEWT6ti0dRK1u5q4efP72LLgXbW7mph7a4Wzp4xhn8+eypTq4t7LMMYQ0tXlI5IfEDTVOpuXRlqdEoK+4epU09Kb4SiCVpDMbqi8b4z58AYw5qdh7nvuTfZebATsJ1YXjh3HJ84a2pennKL/R6qi/NfTaU6DCUfVIehKAUiGrc4EoraYXcH8NuxjOGZNw7ys+d2se9ICLDd6X/wjIlcuXhynwssXCJUFvny8pAMJ74PMaXwqMBQlAITT1i0hmK0D0DPkTz/z68d4Bcv7KKlKwZARdDLJ86aynvnje/RaWUSn8dFTalfl3org0YFhqIcIxKWoS0Uoy0cG1Ao2lA0wW/X1fPA2nrCTnz3qdVFfPadM3jLlMo+zy8Peqkq9umUqjJgVGAoyjHGsgxt4RitoYEJjkMdEf7nuV385fUD3R44zzqpmn99x0lMrOzd3YjX7WJMiV+9JCsDQgWGohwnjDG0heK0hmIDcru+rbGde1bvYMNeOwyw1y18eGEdHzlzcp/OCksCHqqL/X1OZylKKiowFOU4Y4yhPTKwJbnGGJ7ZeoifPLODpnbb3UhtqZ/PvPMk3j5jTK/TT26XUF3i19gbSt6owFCUYYIxhrawLTj6O+IIxxLc/+IeHlhb3x0F8MxpVfzbuTOYUNH7NFWRz8OYEnVoqPSNCgxFGWYkp6qOhKL91nHsawnx46e38+KbhwF7hdRHz5zM5YvqenUd4hKhusRHaSC/JbjK6EQFhqIMUyzL0BqyleP9WY5rjOFv2w9xz1M7ur3iTq0u4obzZ3HqxN79UxX7PYwpUd2GkhsVGIoyzElYttuP9nC8XwaAXdE4P39+Fw+9vA/LgAAXz5/Av7x9GsV+Dy/uPMzKtfU0tIUYXxbkikV1LJ5ehdsl1JT6h8QLrjKyUIGhKCcIsYRl+4sK98/lyNbGdu54fCvbnXj0taV+Ljp1HI9tasTjEgJeF+GYRdwyXH/uTBZPrwLsmOXVarehpNAfgaEaMUU5jnjdLmpLA0ysDPbr63/W2FL+6yNncO050/F7XDS1R/j5C7tpDcXwuV0Itv8oj0tYuba++7y2UIx9R0JE44MLHKWMTlRgKMowwO9xM648wPjyYN4ek90u4fJFdfz3xxdyep2tx+iKJth1uIuOiD1iCXhdHGgLpZ0XjVvsPxLqzqMo+VJQgSEiF4rIGyKyXURuzHH8BhHZJCIbRORJEZmScux7IrJRRDaLyI9Ex9DKKCDoczOpsoiaUj8eV34/z4mVQe64bD6TKoIItn5kf2uYhrYwXZEE48qyl+BaxtDUFuZQR2RAThSV0UnBBIaIuIF7gPcAc4BlIjInI9srwEJjzDzgQeB7zrlnAW8D5gGnAouAdxSqrooy3CgNeKmrClJZlJ++wSXCde+cwZhSH35nhNIejtPQFuYtU3oOD9sWirG/NTzo2ObK6KCQI4zFwHZjzE5jTBRYCVyamsEY87QxJhn+aw0wKXkICAA+wA94gcYC1lVRhh0iQmWxj7rKYF6W24unV/GF805m9thSygIeXAKWgfue28WPntxGuIfQs5FYgn0toQHH+lBGD4VcYzcRqE/Z3wuc2Uv+q4FHAYwxL4jI00AD9srBu40xmzNPEJFrgGsAJk+ePETVVpThhcftorYsQFkswaGOSK8K68XTq7pXRO1rCfGdR7ewqaGNh9fv5+U9R/jaRbOZOTY7PKxlDAdaw1QU+agq9hXsXpQTm2Gh9BaRjwILgdud/RnAKdgjjonAuSLy9szzjDH3GmMWGmMW1tTUHMsqK8oxJ+C19RtjSvMzwptYGeSHV5zOp86ehtsl7DncxWfvf4XfvLinR6PBI11RDrSGB+RxVxn5FFJg7APqUvYnOWlpiMh5wNeAS4wxESf5/cAaY0yHMaYDe+SxpIB1VZQThrKAl0mVRZQE+p4gcLuEK8+czD1XLqCuMkjcMvz0b2/ypQc3cKgjkvOcrmic/UdCROK5p7CU0UshBcZaYKaITBMRH3AFsCo1g4gsAFZgC4umlEN7gHeIiEdEvNgK76wpKUUZrbhdQm2pvQy3N39SSWaNLWXFx97CxfPHA/DKniN86n/X8fyOQznzxxIWDUfCuvRWSaNgAsMYEweuAx7D7ux/a4zZKCLLReQSJ9vtQAnwOxFZLyJJgfIgsAN4DXgVeNUY88dC1VVRTlTsZbhBqop9uPpYTRXwuvn8ebNYfslcygIe2sJxbnp4I3c/vT2nXiS59PZwZ7RQ1VdOMNQ1iKKMEOIJi8Od0bxGBQfbI3z7kc286gRqmjW2hP987xwm9uA2vcjnoSZP3YlyYqGuQRRlFJJcTTWuPNDnNFVNqZ87LpvPx5dMQYCtjR1c+8uXeGbrwZz5Va+hgAoMRRlxFPk8TKoMUh7sPQ6G2yVcddZUbr9sHlXFPrqiCb7+x03c/dT2nIZ8Sb2G2muMXlRgKMoIRMQO1Tqhom/fVGdMruSnH38LM2qKAXjolX28/57nefz1A1l5k/YarV2xgtRbGd6owFCUEUzA62Zihe1ipDe2HeigIxKnzFmq2xVLcNvjb/DrNXty5m/ujHCwXf1QjTZUYCjKCCfpYmRCRc9LcFeurcfrdjGuLMDE8gAuAWPgvufe5Nf/2J3T0K89HONAWxhLjfxGDSowFGWUkBxt5DL4a2gLEfDa3UGx38OUqqJuJ4b3/X0Xt/xhI505Vl+Fogn2t4aIq/PCUYEKDEUZRbgcg7/askCa3cb4siDh2NFO3+t2UVPio9rxK/XcjmY+e/8r7GnuyirTjq8R1hVUowAVGIoyCinxe5hYGSTgdQNwxaI64pYhFEtgsP8nDHzp3Sdzw/mz8LptX1Sfuf9lntuebR0et3QF1WhABYaijFK8bhcTKmwr8TNPqub6c2dSXeynPRynutjfHQv8vfPGc+eHT6e6xF56e/MfNvKrNbuzFN7JFVRtYV1BNVJRS29FUYjEExxs7911+uHOKLes2sjG/W0ALJ1Vw5cuPJmgM0pJRd2knziopbeiKP3C77EV4hW9LL+tKvbx/cvm855TxwGweutBrl+5noPt2V5vj3RFaWoP67LbEYYKDEVRAHv5bVUfy299HhdffPcsrnvnDFwC25s6+Ndfv8zmhrasvB3huC67HWGowFAUJY3k8tvSQG7XIiLCB86YyHc+cBrFfjeHO6N8/rev8tSWpqy8oWiChjYNyDRSUIGhKEoWLpdQU+pnbFmgRw+1i6ZWcc+yM5hYESQat/jmnzfzyxzK8Egswf4jaqsxElCBoShKjxT7PUysOLr8NpPJ1UXcc+UCTq8rB+B/ntvFbX95I0t5HkvYthq9KdWV4Y8KDEVResXjdjG+PEBZD95vy4JebvvgPC6YOxaAxzc18uX/20BbKH15bdyyaGhVF+knMgUVGCJyoYi8ISLbReTGHMdvEJFNIrJBRJ4UkSkpxyaLyOMistnJM7WQdVUUpWdEhDElfmrLAkiOyH5et4svX3Aynzp7GgAb9rbyuZXraWgNpeVLWLatRjiWLjRWb2li2b1rOPu2p1h27xpW59CHKMefggkMEXED9wDvAeYAy0RkTka2V4CFxph52GFZv5dy7BfA7caYU4DFgLYgRTnOlPg9TKjIHaBJRLjyzMnc9E+ndFuGX3f/K2w5kL6CKik0QlFbaKze0sTNqzbS1B6mIuilqT3Mzas2qtAYhhRyhLEY2G6M2WmMiQIrgUtTMxhjnjbGJJ3TrAEmATiCxWOM+auTryMln6Iox5GkzUaRL9uJIcC5s2u5/UPzKA14aOmKccMDr7JmZ3NaHssYDrSF6YjEWfHsTrxuocjnQcT+73ULK57deSxuR+kHhRQYE4H6lP29TlpPXA086mzPAo6IyEMi8oqI3O6MWNIQkWtEZJ2IrDt4MHdoSUVRhh6XSxhXHujR0G/epAp+vGwB48sDhOMWNz38Oo+81pCWxxhDU1uY3c2dWdbiQa+bvS36jTjcGBZKbxH5KLAQuN1J8gBvB74ILAKmA1dlnmeMudcYs9AYs7CmpuYY1VZRlCRVxb4sz7dJJlcV8eNlC5hZW4Jl4I7Ht/K/z+/KWnZbWxqgI8N1eiiWYFJlUUHrrvSfQgqMfUBdyv4kJy0NETkP+BpwiTEm6WNgL7Demc6KAw8DZxSwroqiDJASv4fxPeg1qop93Hn5fBZOqQTgf1/YzV1Pbksz5LtiUR2RuEVbOIYxhq5onFjCcO0504/ZPSj5UUiBsRaYKSLTRMQHXAGsSs0gIguAFdjCoinj3AoRSQ4bzgU2FbCuiqIMAr/HzYSKIEFftr1Gkc/Dt99/KufPsZfd/vHVBr7xp03dNhmLp1dx/bkzqQz6ONwZpabEz/JL5rJ0du0xvQelbwrqrVZELgLuAtzAz4wx3xKR5cA6Y8wqEXkCOA1ITm7uMcZc4px7PvB9QICXgGsc5XlO1FutogwPmjsitIayXZxbxrDimZ387qW9AJxeV8E3Lp1LsT9deV7s91Bb6s+5fFcZevrjrVbdmyuKMuS0h2Mc6ojm9Fb7wNr67hVQs8aW8N0PnJalPA/63IwtDeDqwS2JMnSoe3NFUY4rpQEv48sDeFzZXczli+r4yoUn4xLY2tjB9SvX09gWTsuTdFqonm6HFyowFEUpCAGvmwkVAXye7G7mgrnj+Polc/G6hfqWENevXE/94fRltJFYgv2tIfV0O4xQgaEoSsHwuF1MKM/tvPBtM8bw3Q+cRtDrpqk9wr8/sJ4dBzvS8kTjFvuPqNAYLqjAUBSloLhcwvjyQE7L8AWTK/n+h+dR5liFf/6BV7OCMdmebtU9+nBABYaiKAVHRBhb5qfEny00Zo8r487LT6eyyEtHJM4Xfvcq6+uPpOWJJSwaWsPEVGgcV1RgKIpyTBARassCOSP5TRtTzI+uWMDYMj/hmMWND73G2l2H0/LEEhYNGlPjuKICQ1GUY0pNqT+nD6qJlUHuuvz07gh+Nz38Os/vOJSWR2NqHF9UYCiKcsypKvZRXezPSh9bFuDOy+czpaqIWMJwy6pNPLM13bFo0j26Co1jjwoMRVGOC+VF3pwBmcaU+PnB5fOZXlNMwjJ840+beDojNkZPgZiUwtKrwBCRgIj8u4jcLSLXikhuB/iKoigDoMTvYXx5trfbyiIf379sPjMcT7ffemQzT25uTMujQuPY05cA+F8gBvyNo5Hzri90pRRFGT0EvG7GlQc40BrGSnElUh708v3L5vHlB1/jjcZ2vv3IFhIGKgJeVq6tp6EtxPiyIMsW13Hpgok5bT2UoaVXX1Ii8pox5jRn2wO8aIwZlm7G1ZeUopzYhGOJLKEB0BGO85WHNrC5oR0ByoJeygIeAl4X4ZhF3DL8+7tmqtAYIEPpS6rb5aQTl0JRFKUgJEcamdNTJQEPt31wHnPGl2KA1lCMWNxCEIJeNx6X8JsX63V66hjQl8CYLyJtzl87MC+5LSJtfZyrKIrSL5JCw53hpbbE7+G7H5yH122nH2iP0BaOOee4ONAWsuOEq9AoKL0KDGOM2xhT5vyVGmM8Kdtlx6qSiqKMHgJeN+PLg1mebkv8HmaPLcOXFBptttAIxyzGlQUBVGgUmL5WSX0gZbuy8NVRFEUBn8fFhBxhXz9y5mQqi31pQqM9EueKRUejQavQKBx9TUndlLL9ZH8LF5ELReQNEdkuIjfmOH6DiGwSkQ0i8qSITMk4XiYie0Xk7v5eW1GUExuP28WEimCae/TF06v4/LtmMWtsKR5n2qotFCNmpbsLsYyhsU3diAw1fQkM6WG7T0TEDdzD0eW4y0RkTka2V4CFxph5wIPA9zKOfwN4tj/XVRRl5OB2SZZ79MXTq/jRsgU8+OklnFRTjGVg+Z828eKb6b6nknYa6uV26OhLYARFZIGIvAUIONtnJP/6OHcxsN0Ys9OJxb0SuDQ1gzHmaWNMMmrKGmBS8phzzbHA4/25IUVRRhY9uUcvC3q5/UPzmFJtuxG5edVGXtnTkpbH9j0V1ngaQ0RfAqMB+AFwB3DA2f6+83dHH+dOBOpT9vc6aT1xNfAogIi4nGt8sbcLiMg1IrJORNYdPHiwt6yKopzAJN2jF2e4R68o8nHHh+YxqdJ2WPi1h1/n9X2taXliCYsDGu51SOhrldQ7e/oD/mOoKiEiHwUWArc7SZ8BHjHG7O2jfvcaYxYaYxbW1NQMVXUURRmGiAi1pdlCo7rEzx0fmse4sgDhmMVXf/8a2xrb0/JEYgka28P0Zqis9M1gnA/+to/j+4C6lP1JTloaInIe8DXgEmNMxEleAlwnIruwRzIfF5HvDqKuiqKMAHoSGrVlAe64bB7VJT46Iwm+/H+vsbu5My1PKJqgsS2iQmMQDEZg9KUEXwvMFJFpIuIDrgBWpRUgsgBYgS0sut1RGmM+YoyZbIyZij0t9QtjTNYqK0VRRh9JoeHPcAMyoSLI7R+aR3nQS2soxhcf3EBDaygtT1c0zsH2CMrAGIzA6FVMO65ErgMeAzYDvzXGbBSR5SJyiZPtdqAE+J2IrBeRVT0UpyiK0o2IMLbUn2XcN7W6mNs+eBrFPjfNHVG++LsNNHekC4iOSDwrTcmPvpwP/pHcgkGAc40xxYWqWH9R54OKMvqIxBM0HMl2WPj6vla+9OAGInGLaWOKufPD8ykLpoeGrS7xUx7MDhc72uiP88G+BMY7ejvZGPNMP+tWMFRgKMropCsaz6mbePHNw9z08OvELcOc8aXcftl8ghnTWGPLAln6kNHGUHqrfdMY80xPf0NQV0VRlEFR5PMwpiQ7RvjiaVX8x0WzEWBTQzu3/GEjsQwjvqb2iLoQ6Qd9CYyHkxsi8n8FrouiKEqPrN7SxLJ713D2bU+x7N41rE4J21oa8FJVnC00lp5cy+fPnwnAut0tfPfRLWnTV8ZofPD+0B/XINMLWRFFUZSeWL2liZtXbaSpPUxF0EtTe5ibV21MExoVRb4sPQXAe+dN4FNnTwPg6TcOcvdT29OmryxjaGyNZI0+lGz6Ehimh21FUZRjxopnd+J1C0U+DyL2f69bWPHszrR8Y0r8lOTQSSxbXMcHzrAdTTy8fj+/XLM77XjcsjigLkT6JN8ASqnBkzSAkqIox5T6lq4shXXQ62ZvS1dW3ppSP0Ffel4R4TNLT+Jds2sB+Pnzu/nThoa0POpCpG/yDaCUGjxJAygpinJMqassIpShnA7FEkyqLMrKa9toBNLcogO4RPjKhSezaKod2ueuJ7by3PZDaXnUhUjvDMZwT1EU5Zhw7TnTiSUMXdE4xtj/YwnDtefkVq3aHm6DWQGYPG4Xt148l5PHlmIZ+MafN2c5KwxFE2oN3gMqMBRFGfYsnV3L8kvm4nUJ25o62NsSojhj2ikTt0sYWxbAJelejII+N9/+wKlMrDjq4XZPc/rUllqD50YFhqIoJwxdMYtJlUFm1pYQTVhZK6Uy8XlcjC0LIBlCo7LIx20fPI3KIi/t4Tg3PvQahzujaXlaQzFaQ7GC3MeJigoMRVFOCPJdKZVJ0OemOodh34SKIN9+/2kEPC4OtIX56kOv0RWNp+Vp7ojQGYlnnTtaUYGhKMoJQX9WSmVSFvDmtNE4eVwpN188B5fAtqYOvv7HTVkhXdUa/CgqMBRFOSHoz0qpXFQX+9Jigyd56/RqPn/eLADW7mrhrie3pa2SUmvwo6jAUBTlhKC/K6UyscO8BrJcogP807zxfPStkwF45LUD3P/inrTjag1uM7rdNCqKcsKwdHYty7F1GXtbuphUWcS150xnqWOMl4vVW5pY8exO6lu6qHPyL5lRzf4j2bYWnzxrKgdawzyxuYn7/r6LcWUB3nXK2O7jSWvwCRVB3K6+4seNTHp1b34ioe7NFUVJJel/yusWgl43oViCWMKw/JK5vGVqZU5bi1jC4iv/t4H19a14XMLtl81j/qSKtDx+r5vxZQFcI0RoDKV788FW5EIReUNEtotIVohVEblBRDaJyAYReVJEpjjpp4vICyKy0Tl2eSHrqSjKyKO3VVWlPSjBvW4Xyy85lSlVRcQtwy1/2JilVI/EEjSNUsO+ggkMEXED9wDvAeYAy0RkTka2V4CFxph5wIPA95z0LuDjxpi5wIXAXSJSgaIoSp70taqqJyV4ScDDtz9wKhVBL23hOF996PUse4zRGhu8kCOMxcB2Y8xOY0wUWAlcmprBGPO0MSYpvtcAk5z0rcaYbc72fqAJqClgXRVFGWH0taqqNyX4+PIg33zfqfg8LvYdCXHzHzYSjacrvNvDMY50RbPOHckUUmBMBOpT9vc6aT1xNfBoZqKILAZ8wI4cx64RkXUisu7gwYODrK6iKCOJzFVVB9vD7G0Jsa2pvTsAk9sl1Jb5syzBAeZMKOPGC2cD8Nq+Vu58YmuWovxwZ3RUGfYNi2W1IvJRYCFwe0b6eOCXwCeNMVnr2Ywx9xpjFhpjFtbU6ABEUZSjJP1P1ZYGONAaoqUrRlWxl3FlgbQATAGvm8qibH0GwNKTa7j67KkAPLaxkd+8WJ+VZzQZ9hVSYOwD6lL2JzlpaYjIecDXgEuMMZGU9DLgz8DXjDFrClhPRVFGKEtn1/Kba97KzLFlTKoMMqYkkNOtSEWRjyJfbiuDKxdP5vw59vLa//77mzy7LX02wxhDU1sky0J8JFJIgbEWmCki00TEB1wBrErNICILgBXYwqIpJd0H/B74hTHmwQLWUVGUUUA+bkVqSv1Z7tDB1nV84fxZnDrBDgH0nUe2sLWxPS1P3BodwZcKJjCMMXHgOuAxYDPwW2PMRhFZLiKXONluB0qA34nIehFJCpQPA+cAVznp60Xk9ELVVVGUkU0+bkXcLqGmNLc+w+dxsfzSuYwvDxCJW9z08OtZ7s+jcWvEL7dVwz1FUUY8vRnxZVqKH+mKcrgzyos7D7NybT0NbSHGlwW5YlEdNWV+/u03r9AVTTB7XCl3fng+/oyRS1nQy5gS/7G8vUExbAz3FEVRhgOpCvDWUIza0kBOYQG2PuPVPUf44VPbaO6MUBbw0NwZ4YdPbeNgW4Sb/ukUBNhyoJ07Hs9eOdUWitHaNTLjaKgvKUVRRgVLZ9f26ncqlQfW1eN1CwGPPXpIjkpWrq3nB5fP55pzprPi2Z08uaWJaWOKufLMyWnnN3dG8HqkR0X6icrIuhtFUZQhYO+REGV+D/EUJXbA6+JAWwiADy+cxK7mTh7b2Mh9f3+TaWOKWXJSdVoZTW0RJlS48Hn6nsjJ5SQxX+F2LNEpKUVRlAzqKouIJKw0r7ThmMW4siBgr5z6/HmzmDO+FAN865HN7G7uTCvDMobGtjCJPlZOJfUrTe1hKoLeNBuR4YYKDEVRlAySVuLJoEmhWIK4Zbhi0VHTMp/HxdcvmcuYEh9d0QQ3PbyR9nC67iKWsGhsy3alnspAQ88eD1RgKIqiZJBUko8tC9IVjVNd4uf6c2eyeHpVWr7qEj/LL52L1y3sOxLiG3/anDWiCMcSHOqIsnpLE8vuXcPZtz3V7ZoEBhd69lijOgxFUZQcpCrJ28OxHr3Tzh5XxhfffTLfeXQL63a3cN/f3+SajCiAT25q5O6nt+P3utKmnZZjT381tYfTFOT9CT17LNERhqIoSh+UBryU+Hv+vj5/zlgue8skAFaurefJzen6h5Vr6xGBgMedNe002NCzxxIVGIqiKHkwpiS365Ak15wznbdMtsP23PH4G2xLcR/S0BYi4HURs6xufUZy2qk/NiLHG52SUhRFyQOX4zpk/5FQzuNul3DTe+fwmV+/TENrmFtWbeK/PnoG5UEv48uCNHdGCHrdxBIGrzt92qk/NiLHEx1hKIqi5EnA66aiyNfj8fKgl+WXziXgcXGgLcw3/2wrwa9YVEfcMhzujLDncCebG9rY2xJiSYYSfbijAkNRFKUfVBZ5s/xHpXJSvPFCUQAAFD5JREFUTQlfvOBkAF5ylOCLp1dx4ZyxtIbjROIWXrdQEfTw4Mv7hqW9RU+owFAURekHIkJNiR9XDq+2Sc6dXZumBH9m60FeqW9lXFmAGTUlTK4qpjzowy0MS3uLnlCBoSiK0k98HhdVJT1PTYGtBD+9zlaCf+8vb1Df0knAm97letxC/eHOXKcPS1RgKIqiDICygJfiXpbaul3Cf773FGpK/IRiCToiCbqi6TE5wjGLmtJAn+5DhgsqMBRFUQbImBJ/mr+pTCqLfNx6yRw8LiESt2juiNr2FphudyOXL6yjqb139yHDhYIKDBG5UETeEJHtInJjjuM3iMgmEdkgIk+KyJSUY58QkW3O3ycKWU9FUZSBkIzS1xunjC/junNnABCOW1gG2sNxqouPuhsJRW33IcOdgtlhiIgbuAc4H9gLrBWRVcaYTSnZXgEWGmO6RORfge8Bl4tIFXALsBAwwEvOuS2Fqq+iKMpAKPJ5KA14sxwPpnLxvPFsbmjjsY2NHGgL8/0Pz2f+pIq0PO3hGD63i/Iib6GrPGAKOcJYDGw3xuw0xkSBlcClqRmMMU8bY5IettYAk5ztC4C/GmMOO0Lir8CFBayroijKgKku9vVqBS4iXP+umZxUU4xl4Bt/2pwVExzswEtd0XghqzooCikwJgL1Kft7nbSeuBp4tD/nisg1IrJORNYdPHhwkNVVFEUZGK48pqYCXje3XjyXYp+bw51RvvHnbM+2YAdeisatQlV1UAwLpbeIfBR7+un2/pxnjLnXGLPQGLOwpqamMJVTFEXJg4DXTXmw9+mkiZVBbnzPbAA27G3lv/+WbYORDLxkDcOVU4UUGPuAupT9SU5aGiJyHvA14BJjTKQ/5yqKogwnqvqYmgJ424wx3YGYHli3l+e2H8rKE0tYNPXgTv14UkiBsRaYKSLTRMQHXAGsSs0gIguAFdjCItU+/jHg3SJSKSKVwLudNEVRlGGLSN9TUwBXnz2N0yaWA3DbX96goTXboWFX9P+3d+9BUlZnHse/v+m5z6DMjBBREbywAhqvA94Jxo3rZQtWo+UtWdG4aq2WJntxTbTcCiYbDUltYtZYupqKbtRojIl4W7xrdJfFEUGEgYiKihEhOiojt2F49o9zRpumh35x5p3uHp5PVde8/b7n7X6OLf30e857ztnIB5+U1p1TqSUMM9sIXEL4om8H7jGzhZKmS5oSi80AGoHfSJonaWY89wPgGkLSeQGYHvc551xJS9I01TOor6m+is71G5n+QHvefosP12ygc33pdIKrHAaLJNHa2mptbW3FDsM55zAzlnespat7653Xc9/s4J/vfRkDph64C5cdO2aLMpIYsWMttVuZ8LAvJL1oZq1JypZEp7dzzg0mSZumDh7VxDlHhPHK98/7E08v2fJuTzNj5cfr2Vgg+QwETxjOOZeC2qoMOxRomgI4+9BRHDKqCQgr9b3TsWV/xsZNm3hv9fotpg95evFKzrx5Nkdd9yRn3jw79anSPWE451xKmusL3zWVqRDfOXEsLQ3VrNnQzXcfXJS3P2N91+bThzy9eCVXz1zIytXrGFpXxcrV67h65sJUk4YnDOecS0lFhdipsXDTVFN9NVeeNI4KwdKVndz4zGt5y61e1/XpFCQ3Pfs6VRlRX12JFP5WZZTq+hqeMJxzLkV11RmG1BZumjpw5FDOOXw0EPoznn01/+wVf+7cwPqN3bzdsYa6nI7wuqoMyzvW5D2vP3jCcM65lLU0VFNZUfjr9qxDd+fg3cOkhDNmLWHFR+u2KNPTCb7b0DrWdm2+vsbarm52a6rvn6Dz8IThnHMpq6gQOw3Z+gp90NOfEcZnfLK+m2seWpT37qiu7k2c3jqSrm4L62tY+NvVbVw4ac80qgB4wnDOuQFRX11J41ZW6OvR3FDNt08Yi4D2d1dz63Nv5C13wO5Dufy4fRg+pJaP1nYxfEgt06fsy+Sxw/s58s+kth6Gc865zbXE5VoLLcnaOrqZMyeO5M45b3N323IOHtXEhNHNW5Qbv+sO3Dqtlfrqgfkq9ysM55xLQb4xEpmEd00BnHvkHowfsQMA1z6yuNd5pVblGZ+RFk8YzjnXz7Y2RqKhJlnTVKZCXHXSOBpqMnSs6eLaRxazKU9iKHS10p88YTjnXD8rNEaipbEm0V1TO+9Yyz9+ZR8A2t7s4J625anGXYgnDOec62eFxkhkKkRLY+G7pgAm7zOMk744AoBbn3uDJStW92+w28AThnPO9bORTfUFx0gkbZoCuPiYvRjVXE/3JuN7D7WzdkN34ZNS4AnDOef62YWT9kw0RqKlsYZMhQq+Xm1VhqtOGkdVRrzz4Vquf/LVtELfKk8YzjnXzyaPHc70KfsWHCORqRDNDcmapvYa3sgFMeHMWvgeT6Y8M20+qd68K+l44KdABrjFzK7NOT4J+AmwP3CGmd2bdeyHwEmEpPYYcJkNltWenHOD3uSxwxMNohtSG0Z1r9lQeGW9Uw7alReWdTDnjQ/498f+yPgRO7DzjrX9EW4iqV1hSMoANwAnAOOBMyWNzyn2FjANuDPn3COAIwmJZD9gAvCltGJ1zrliammsRircNCWJfzl+nzB1yIZufvBI+6C5rXYisNTMXjezDcCvganZBcxsmZm9DOROlmJALVAN1ABVwHspxuqcc0VTlamgqb7wjLYQpkK//Phwq+2Cdz7mrjlvpRnaZtJMGLsCb2c9Xx73FWRm/ws8BbwbH7PMrL3fI3TOuRKxY11VwcWWehy6RwsnHxS+Tn/5P8uY9/aHaYb2qZLs9Ja0NzAO2I2QZL4s6eg85S6Q1CapbdWq/HPHO+dcOZCSTxsCcMHRezC6pZ5NBt+6ex6frC/cB9JXaSaMd4CRWc93i/uSOBmYbWadZtYJPAIcnlvIzG42s1Yzax02bFifA3bOuWKqq84kHptRU5Xhynir7bL31/DUkvTvmkozYbwAjJG0h6Rq4AxgZsJz3wK+JKlSUhWhw9ubpJxzg15zQzUVCTrAAfYa1shlx47h9vMm8Nf775JyZCkmDDPbCFwCzCJ82d9jZgslTZc0BUDSBEnLgdOAmyQtjKffC7wGLADmA/PN7IG0YnXOuVJRmamgOeG0IQAnfnEER48ZmBaWVMdhmNnDwMM5+67O2n6B0FSVe143cGGasTnnXKnaobaKznUbWddVnClAelOSnd7OObe926mxJtHYjIHkCcM550pQdWUFQ+uSjc0YKJ4wnHOuRA2tTz42YyCUTiTOOec2I4lhQ5KPzUibJwznnCthtVUZhtSWRtOUJwznnCtxLQ3ViZZ0TVvxI3DOObdVFduwpGuqcRQ7AOecc4U11FRSX53q0LmCPGE451yZSLpuRlo8YTjnXJnYlnUz0uAJwznnysiOdVVUVxbnq9sThnPOlZFtXTejP3nCcM65MlNblaGxduA7wD1hOOdcGWppqEm8bkZ/8YThnHNlKFMhmhoGdmyGJwznnCtTA90Bnuo7STpe0hJJSyVdkef4JElzJW2UdGrOsd0lPSqpXdIiSaPTjNU558rRQHaAp5YwJGWAG4ATgPHAmZLG5xR7C5gG3JnnJW4HZpjZOGAikP4K5845V2ZqqzIDNpgvzW72icBSM3sdQNKvganAop4CZrYsHtuUfWJMLJVm9lgs15linM455xJIs0lqV+DtrOfL474k/gL4UNJ9kl6SNCNesTjnnCuSUu30rgSOBv4JmADsSWi62oykCyS1SWpbtWrVwEbonHPbmTQTxjvAyKznu8V9SSwH5pnZ62a2Efg9cHBuITO72cxazax12LBhfQ7YOedc79JMGC8AYyTtIakaOAOYuQ3nDpXUkwW+TFbfh3POuYGXWsKIVwaXALOAduAeM1soabqkKQCSJkhaDpwG3CRpYTy3m9Ac9YSkBYCA/0wrVuecc4XJzIodQ79obW21tra2YofhnHNlRdKLZtaapGypdno755wrMZ4wnHPOJTJomqQkrQLeLHYcCe0E/LnYQfSzwVYnr09p8/r0n1Fmlug200GTMMqJpLakbYblYrDVyetT2rw+xeFNUs455xLxhOGccy4RTxjFcXOxA0jBYKuT16e0eX2KwPswnHPOJeJXGM455xLxhOGccy4RTxgpkvQtSQslvSLpLkm1OcenSVolaV58nF+sWJOQdFmsy0JJ38xzXJKuj0vyvixpixmGS0mC+kyW9FHW53N1MeLcGkm/kLRS0itZ+5olPSbp1fi3qZdzz4llXpV0zsBF3bs+1qc767NKOtFpqnqpz2nx/7lNknq9lbbQEtdFYWb+SOFBWCzqDaAuPr8HmJZTZhrwH8WONWF99gNeAeoJ65U8DuydU+ZE4BHCZJGHAf9X7Lj7WJ/JwIPFjrVAPSYRpv5/JWvfD4Er4vYVwHV5zmsGXo9/m+J2U7nWJx7rLHb8CeszDtgHeBpo7eW8DPAaYS2gamA+ML7Y9fErjHRVAnWSKglfTH8qcjx9MY6QANZYmIn4GeCUnDJTgdstmE2Yon7EQAeaUJL6lDwzexb4IGf3VOC2uH0b8Dd5Tv0r4DEz+8DMOoDHgONTCzShPtSnJOWrj5m1m9mSAqd+usS1mW0Aepa4LipPGCkxs3eAHwFvAe8CH5nZo3mKfjU239wraWSe46XiFeBoSS2S6glXE7nx9mVZ3oGWpD4Ah0uaL+kRSfsObIif2xfM7N24vQL4Qp4y5fRZJakPQG1cgXO2pLJJKr0oyc/HE0ZKYjvrVGAPYBegQdLXcoo9AIw2s/0Jv/Buo0SZWTtwHfAo8N/APKC7qEH1QcL6zCXMs3MA8DPCyo9lxUL7xqC5d75AfUZZmF7jLOAnkvYauMi2D54w0vOXwBtmtsrMuoD7gCOyC5jZ+2a2Pj69BThkgGPcJmZ2q5kdYmaTgA7gjzlF+rIs74ArVB8z+9jMOuP2w0CVpJ2KEOq2eq+nKTD+XZmnTDl9Vknq03NVj5m9TugfOGigAkxBSX4+njDS8xZwmKR6SQKOJaw8+Kmc9v0pucdLjaTh8e/uhPb+O3OKzAT+Nt4tdRihGe5dSlSh+kjaOX52SJpI+Pfy/kDH+TnMBHruejoHuD9PmVnAcZKa4tXwcXFfKSpYn1iPmri9E3Ak5b2sc1+WuE5PsXvdB/MD+C6wmNBe/l9ADTAdmBKP/wBYSLgD4ilgbLFjLlCfPxD+Ec4Hjo37LgIuitsCbiDc3bGAXu4AKZVHgvpckvX5zAaOKHbMeepwF6GPrIvQzv0NoAV4AniVcPdXcyzbCtySde55wNL4OLfYdelLfQhX7wviZ7UA+Eax67KV+pwct9cD7wGzYtldgIezzj2RcNX7GnBlsetiZj41iHPOuWS8Sco551winjCcc84l4gnDOedcIp4wnHPOJeIJwznnXCKeMFxZkNTZx/PvlbRn3G6UdKOk1yTNlfSipL+Lx0ZLWivpJUntkuZImpb1OtkzDC/qOa8cSLpF0vi4/Z2E5zze2+ywbvvjCcMNenEOqIyFEcAQRtV3AGPM7GDCpHvNWae8ZmYHmdk4woCpb0o6N+v43WZ2IGE223+T1NvcRp833kx/vl4PMzvfzHoGsyVKGITxQ3+fRjyu/HjCcGUljiKfEdexWCDp9Li/QtLPJS2OayY8LOnUeNrZxNHBcX6hicBVZrYJwML0Ldfle7+YZP4BuDTPsZWEQVWjcmKcJul+SU/HNRz+NevY1+JVyzxJN/UkB0mdkn4saT5weM7r7R1/6c+PV0R7xaukJ+LzBZKmxrKj43+DO+IV0r1xckViPK2SriXMojxP0h3x2O/jldZCSRdkvf1M4MzCn4zbHnjCcOXmFOBA4ADCfF0z4hQrpwCjgfHA19n8S/dI4MW4vS8wvydZJDQXGJu7MzZx7UkYKZ1rIvBVYH/gtPhFPQ44HTgyXqF0E5IZQANhuvUDzOy5nNe6A7jBwiSIRxBGDq8DTo5XSMcAP+6ZxoSw1sLP4xXSx+RcIZjZFcBaMzvQzHre/zwzO4QwevpSSS2xbAdQ0/Pcbd8qix2Ac9voKOAuM+smTEr3DDAh7v9NTAQrJD2Vdc4IYFW+F5N0JXAaMNzMdunlPZXz/HRJRxGmdrjQzHLXb4Cw1sT78T3ui/FtJEww+UL8bq/js4n0uoHf5olvCLCrmf0OwMzWxf1VhOawScAmwtTXPU1jb5vZ83H7V4Srox/1Urcel0o6OW6PBMbw2bxZKwnTVpTDPFouRZ4w3PZgLdCzPO4i4ABJFWa2ycy+D3y/QKf6QWw+MeTdZnZJgffMnXPHCInnNjP7dp7y62ISTOpsYBhwiJl1SVrGZ3XM9969kjSZcLV2uJmtkfR01msRt9duQ2xukPImKVdu/kD4hZ+RNIywBOYc4HnCYlQVsRN6ctY57cDeAGa2FGgDvpfVf1DLllcRxGOjCb/Of7aNcX5FYS3qOsIKcc8TJtA7NWuW3GZJo7b2Ima2GliuuCCQpJrYJ7EjsDImi2PYvB9ld0k9TXJnAblNXABd8SqF+FodMVmMJSyvS3w/ATsDy7al8m5w8isMV25+R+ifmE/45Xy5ma2Q9FvCFPKLCCuVzQU+iuc8REggj8fn5wMzgKWS3if8er486z32kvQS4Zf1auB6M/vlNsY5h9DEtBvwKzNrA5B0FfCopArCDKYXA28WeK2vAzdJmh7POY3Qr/GApAWEBLg4q/wS4GJJvyD897gxz2veDLwsaS5h1tqLJLXHc2dnlTsEmG1hGVu3nfPZat2gIanRzDpjB+0cQufyivgr/6n4PPVVAuO4jdYEzVZpvPdo4EEz26+fXu+nwEwze6I/Xs+VN7/CcIPJg5KGAtXANWa2AsDM1sZbW3clLGzlknvFk4Xr4VcYzjnnEvFOb+ecc4l4wnDOOZeIJwznnHOJeMJwzjmXiCcM55xzifw/JoeH+0YuTygAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cummulative_model = draw_extended_reg_plot(df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "id": "UQrJDemBZ7HL",
+        "outputId": "25c68472-0d2c-416a-8f06-3907d4680cb5"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(cummulative_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5AQvx5_waII-",
+        "outputId": "5580c86d-363c-494c-f044-9653a756f8da"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "          2\n",
+            "-0.02601 x + 0.4626 x - 1.745\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_r2_score(df, cummulative_model))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qhRE4vgSaP56",
+        "outputId": "8d20854f-6a45-461c-9eed-70b3c60447f7"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9538936281313893\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(get_coorelation(df))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qtl3hIpmGvaY",
+        "outputId": "e2600a18-815c-41b6-bf46-1adbf38afdfc"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9020312937686403\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> **Regression result (cummulative)**\n",
+        "\n",
+        "| Variable      | Coefficient |\n",
+        "| ----------- | ----------- |\n",
+        "| (log GDP per capita)^2      | ß0 = -0.02601       |\n",
+        "| log GDP per capita   | ß1 = 0.4626        |\n",
+        "| R-squared      | 0.9538936281313893       |\n",
+        "| α   | -1.745        |\n",
+        "\n",
+        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
+        "\n",
+        "\n",
+        "> **Coorelation (cummulative)**\n",
+        "\n",
+        "Coorelation between FLFP and log GDP per capita: **0.9020312937686403**\n",
+        "\n",
+        "*This shows strong coorelation*"
+      ],
+      "metadata": {
+        "id": "_KaWc-2nhwVt"
+      }
+    }
+  ]
+}
\ No newline at end of file

From d76f60e31e4988b4b25089215483dd6d085961b4 Mon Sep 17 00:00:00 2001
From: SHARVAN JEET <imsharvanj@gmail.com>
Date: Sat, 16 Jul 2022 18:37:02 +0530
Subject: [PATCH 3/3] Delete shreya_flfpr_gdp.ipynb

---
 shreya_flfpr_gdp.ipynb | 1502 ----------------------------------------
 1 file changed, 1502 deletions(-)
 delete mode 100644 shreya_flfpr_gdp.ipynb

diff --git a/shreya_flfpr_gdp.ipynb b/shreya_flfpr_gdp.ipynb
deleted file mode 100644
index c15abba..0000000
--- a/shreya_flfpr_gdp.ipynb
+++ /dev/null
@@ -1,1502 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "shreya_flfpr_gdp.ipynb",
-      "provenance": [],
-      "authorship_tag": "ABX9TyOfuEjUih0SdYqCGQgPcV6A",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/imsharvanj/2048/blob/master/shreya_flfpr_gdp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fJbayRGGokUm",
-        "outputId": "39901c45-64a9-4de6-eba4-b97afde4c566"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/gdrive\n"
-          ]
-        }
-      ],
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/gdrive', force_remount=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.metrics import r2_score\n",
-        "import scipy.stats as stats"
-      ],
-      "metadata": {
-        "id": "FaZsv8-lwupx"
-      },
-      "execution_count": 20,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df = pd.read_csv('gdrive/My Drive/_python notebooks/data.csv')\n",
-        "df.rename(columns={'GDP per capita': 'gdp_per_capita', 'Year': 'year'}, inplace=True)\n",
-        "df.name = 'Cummulative Data'\n",
-        "df.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Eee-kd3xpOD0",
-        "outputId": "cab8b1c3-e6e8-46cc-8f13-34850f3101ec"
-      },
-      "execution_count": 21,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   year  gdp_per_capita      flfpr\n",
-              "0  1990      367.556609  30.443001\n",
-              "1  1991      303.055608  30.452999\n",
-              "2  1992      316.953927  30.493000\n",
-              "3  1993      301.159002  30.570000\n",
-              "4  1994      346.102951  30.691999"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-c804290d-949b-471f-8dc1-9f524b0314b0\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>year</th>\n",
-              "      <th>gdp_per_capita</th>\n",
-              "      <th>flfpr</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1990</td>\n",
-              "      <td>367.556609</td>\n",
-              "      <td>30.443001</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1991</td>\n",
-              "      <td>303.055608</td>\n",
-              "      <td>30.452999</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1992</td>\n",
-              "      <td>316.953927</td>\n",
-              "      <td>30.493000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1993</td>\n",
-              "      <td>301.159002</td>\n",
-              "      <td>30.570000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1994</td>\n",
-              "      <td>346.102951</td>\n",
-              "      <td>30.691999</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c804290d-949b-471f-8dc1-9f524b0314b0')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
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-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
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-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-c804290d-949b-471f-8dc1-9f524b0314b0 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-c804290d-949b-471f-8dc1-9f524b0314b0');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 21
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df['log_gdp_per_capita'] = np.log2(df['gdp_per_capita'])\n",
-        "df['log_gdp_per_capita_squared'] = df['log_gdp_per_capita']**2\n",
-        "df['abs_flfp'] = df['flfpr']/100\n",
-        "df.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "fvGBL89fpP7O",
-        "outputId": "f5774197-ce81-45ae-e8fd-6d2c24fba5ea"
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
-              "0  1990      367.556609  30.443001            8.521823   \n",
-              "1  1991      303.055608  30.452999            8.243439   \n",
-              "2  1992      316.953927  30.493000            8.308129   \n",
-              "3  1993      301.159002  30.570000            8.234382   \n",
-              "4  1994      346.102951  30.691999            8.435057   \n",
-              "\n",
-              "   log_gdp_per_capita_squared  abs_flfp  \n",
-              "0                   72.621461   0.30443  \n",
-              "1                   67.954282   0.30453  \n",
-              "2                   69.025013   0.30493  \n",
-              "3                   67.805040   0.30570  \n",
-              "4                   71.150194   0.30692  "
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-              "      <th>year</th>\n",
-              "      <th>gdp_per_capita</th>\n",
-              "      <th>flfpr</th>\n",
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-              "      <th>log_gdp_per_capita_squared</th>\n",
-              "      <th>abs_flfp</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1990</td>\n",
-              "      <td>367.556609</td>\n",
-              "      <td>30.443001</td>\n",
-              "      <td>8.521823</td>\n",
-              "      <td>72.621461</td>\n",
-              "      <td>0.30443</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1991</td>\n",
-              "      <td>303.055608</td>\n",
-              "      <td>30.452999</td>\n",
-              "      <td>8.243439</td>\n",
-              "      <td>67.954282</td>\n",
-              "      <td>0.30453</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>1992</td>\n",
-              "      <td>316.953927</td>\n",
-              "      <td>30.493000</td>\n",
-              "      <td>8.308129</td>\n",
-              "      <td>69.025013</td>\n",
-              "      <td>0.30493</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1993</td>\n",
-              "      <td>301.159002</td>\n",
-              "      <td>30.570000</td>\n",
-              "      <td>8.234382</td>\n",
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-              "      <td>0.30570</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1994</td>\n",
-              "      <td>346.102951</td>\n",
-              "      <td>30.691999</td>\n",
-              "      <td>8.435057</td>\n",
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-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc682b88-734f-451e-a8dd-81282f2ef209')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
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-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-dc682b88-734f-451e-a8dd-81282f2ef209 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-dc682b88-734f-451e-a8dd-81282f2ef209');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 22
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "### **Recession happened between in 2006-2009. So we are dividing data in 2 parts.**\n"
-      ],
-      "metadata": {
-        "id": "n5OUv123V8Nj"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# before 2006\n",
-        "df_before_2006 = df[df['year'] < 2006]\n",
-        "df_before_2006.name = 'Before 2006'\n",
-        "df_before_2006.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "WWN_yLusQ5oi",
-        "outputId": "30bd9540-2fc0-42b2-bda6-053a77c8589a"
-      },
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
-              "0  1990      367.556609  30.443001            8.521823   \n",
-              "1  1991      303.055608  30.452999            8.243439   \n",
-              "2  1992      316.953927  30.493000            8.308129   \n",
-              "3  1993      301.159002  30.570000            8.234382   \n",
-              "4  1994      346.102951  30.691999            8.435057   \n",
-              "\n",
-              "   log_gdp_per_capita_squared  abs_flfp  \n",
-              "0                   72.621461   0.30443  \n",
-              "1                   67.954282   0.30453  \n",
-              "2                   69.025013   0.30493  \n",
-              "3                   67.805040   0.30570  \n",
-              "4                   71.150194   0.30692  "
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-              "      <th>2</th>\n",
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-              "      <th>4</th>\n",
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-              "\n",
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-              "      background-color: #434B5C;\n",
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-              "\n",
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-              "        const buttonEl =\n",
-              "          document.querySelector('#df-a47d2933-639e-497e-8977-826a689f980b button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-a47d2933-639e-497e-8977-826a689f980b');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
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-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# after 2006\n",
-        "df_after_2006 = df[df['year'] >= 2006]\n",
-        "df_after_2006.name = 'After 2006'\n",
-        "df_after_2006.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "BQIDOa43RMNY",
-        "outputId": "59ee221d-ee93-4d23-a4b5-8dfc63920bc0"
-      },
-      "execution_count": 24,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "    year  gdp_per_capita      flfpr  log_gdp_per_capita  \\\n",
-              "16  2006      806.753281  30.712000            9.655984   \n",
-              "17  2007     1028.334772  29.490000           10.006094   \n",
-              "18  2008      998.522342  28.290001            9.963651   \n",
-              "19  2009     1101.960838  27.114000           10.105857   \n",
-              "20  2010     1357.563727  25.965000           10.406804   \n",
-              "\n",
-              "    log_gdp_per_capita_squared  abs_flfp  \n",
-              "16                   93.238022   0.30712  \n",
-              "17                  100.121923   0.29490  \n",
-              "18                   99.274339   0.28290  \n",
-              "19                  102.128351   0.27114  \n",
-              "20                  108.301574   0.25965  "
-            ],
-            "text/html": [
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-              "      <div>\n",
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-              "      <th></th>\n",
-              "      <th>year</th>\n",
-              "      <th>gdp_per_capita</th>\n",
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-              "      <th>log_gdp_per_capita</th>\n",
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-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>2006</td>\n",
-              "      <td>806.753281</td>\n",
-              "      <td>30.712000</td>\n",
-              "      <td>9.655984</td>\n",
-              "      <td>93.238022</td>\n",
-              "      <td>0.30712</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>2007</td>\n",
-              "      <td>1028.334772</td>\n",
-              "      <td>29.490000</td>\n",
-              "      <td>10.006094</td>\n",
-              "      <td>100.121923</td>\n",
-              "      <td>0.29490</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>2008</td>\n",
-              "      <td>998.522342</td>\n",
-              "      <td>28.290001</td>\n",
-              "      <td>9.963651</td>\n",
-              "      <td>99.274339</td>\n",
-              "      <td>0.28290</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>2009</td>\n",
-              "      <td>1101.960838</td>\n",
-              "      <td>27.114000</td>\n",
-              "      <td>10.105857</td>\n",
-              "      <td>102.128351</td>\n",
-              "      <td>0.27114</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>20</th>\n",
-              "      <td>2010</td>\n",
-              "      <td>1357.563727</td>\n",
-              "      <td>25.965000</td>\n",
-              "      <td>10.406804</td>\n",
-              "      <td>108.301574</td>\n",
-              "      <td>0.25965</td>\n",
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-              "  </tbody>\n",
-              "</table>\n",
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-              "              title=\"Convert this dataframe to an interactive table.\"\n",
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-              "\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-ec9a31e7-84eb-480a-8f34-cd319c38cade button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-ec9a31e7-84eb-480a-8f34-cd319c38cade');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
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-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 24
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def draw_scatter_plot(df): \n",
-        "  sns.scatterplot(x='log_gdp_per_capita',y='abs_flfp',data=df) \n",
-        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
-        "  plt.ylabel('FLFP')\n",
-        "  plt.xlabel('log(GDP per capita)')\n",
-        "  plt.show()"
-      ],
-      "metadata": {
-        "id": "gD5_B5qStmMH"
-      },
-      "execution_count": 25,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def draw_reg_plot(df):\n",
-        "  sns.regplot(x='log_gdp_per_capita',y='abs_flfp',data=df, order=2) \n",
-        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
-        "  plt.ylabel('FLFP')\n",
-        "  plt.xlabel('log(GDP per capita)')\n",
-        "  plt.show()"
-      ],
-      "metadata": {
-        "id": "Jw3wmMfWSGIz"
-      },
-      "execution_count": 26,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def draw_extended_reg_plot(df):\n",
-        "  # degree 2 polynomial fit or quadratic fit\n",
-        "  model = np.poly1d(np.polyfit(df['log_gdp_per_capita'],\n",
-        "                              df['abs_flfp'], 2))\n",
-        "    \n",
-        "  # polynomial line visualization\n",
-        "  polyline = np.linspace(0, 20, 100)\n",
-        "  plt.scatter(df['log_gdp_per_capita'], df['abs_flfp'])\n",
-        "  plt.plot(polyline, model(polyline))\n",
-        "  plt.title('Female Labour Force Participation vs log(GDP per capita)\\n' + df.name)\n",
-        "  plt.ylabel('FLFP')\n",
-        "  plt.xlabel('log(GDP per capita)')\n",
-        "  plt.show()\n",
-        "  return model\n",
-        "  "
-      ],
-      "metadata": {
-        "id": "jTJvdOCqyyQV"
-      },
-      "execution_count": 27,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def get_r2_score(df, model):\n",
-        "  return r2_score(df['abs_flfp'], \n",
-        "               model(df['log_gdp_per_capita']))"
-      ],
-      "metadata": {
-        "id": "axwdZ6VvXb-Z"
-      },
-      "execution_count": 28,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def get_coorelation(df):\n",
-        "  return abs(df['log_gdp_per_capita'].corr(df['abs_flfp']))"
-      ],
-      "metadata": {
-        "id": "5WN8pN-DEWQY"
-      },
-      "execution_count": 29,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# **BEFORE 2006**"
-      ],
-      "metadata": {
-        "id": "cPLc-wrrYNkm"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_scatter_plot(df_before_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "Txldtlu6YWUA",
-        "outputId": "3fce28bc-2822-4289-c0d1-e7f0729e76c3"
-      },
-      "execution_count": 30,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_reg_plot(df_before_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "77SgC4_LUszN",
-        "outputId": "2b7ce4c6-577d-4df4-c411-ca5bd34385b3"
-      },
-      "execution_count": 31,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model_before_2006 = draw_extended_reg_plot(df_before_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "8FHEfW7XVeOH",
-        "outputId": "ad90e8c3-2f32-4639-de44-e966bfe88108"
-      },
-      "execution_count": 32,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(model_before_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "sF1KDJ0zYjuu",
-        "outputId": "6a4f930d-c40c-4941-f83e-659f5ce8c0ac"
-      },
-      "execution_count": 33,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "         2\n",
-            "0.01261 x - 0.211 x + 1.188\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_r2_score(df_before_2006, model_before_2006))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YQZ9KOp8Ytw8",
-        "outputId": "7775e97f-fa34-44c2-be07-75ad9192f22e"
-      },
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9093691704816828\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_coorelation(df_before_2006))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "V0WtDWp-FO5e",
-        "outputId": "82ff6640-ef15-462a-df63-9e04b0a6d52d"
-      },
-      "execution_count": 35,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.8674146211561067\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "> **Regression result before 2006**\n",
-        "\n",
-        "| Variable      | Coefficient |\n",
-        "| ----------- | ----------- |\n",
-        "| (log GDP per capita)^2      | ß0 = 0.1261       |\n",
-        "| log GDP per capita   | ß1 = -0.211        |\n",
-        "| R-squared      | 0.9093691704816828       |\n",
-        "| α   | 1.188        |\n",
-        "\n",
-        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
-        "\n",
-        "\n",
-        "> **Coorelation result before 2006**\n",
-        "\n",
-        "Coorelation between FLFP and log GDP per capita: **0.8674146211561067**\n",
-        "\n",
-        "*This shows strong coorelation*"
-      ],
-      "metadata": {
-        "id": "wKp06W9lfyjt"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# **AFTER 2006**"
-      ],
-      "metadata": {
-        "id": "aobre6FYY4H2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_scatter_plot(df_after_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "lD4XYeHvZUGJ",
-        "outputId": "afeb66c6-bdd5-4ae4-9366-21d0a2bae0ff"
-      },
-      "execution_count": 36,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_reg_plot(df_after_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "WGNMSPEPWtU6",
-        "outputId": "780da54a-7ef3-49d9-dc55-ee006faee247"
-      },
-      "execution_count": 37,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model_after_2006 = draw_extended_reg_plot(df_after_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "4OQFu29lWz0T",
-        "outputId": "f49b4270-e68c-434b-c304-83d2a85587f1"
-      },
-      "execution_count": 38,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(model_after_2006)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7J8nOHnQAbt3",
-        "outputId": "6ae37af1-27c6-483c-8bd2-aeb0c7338ce1"
-      },
-      "execution_count": 39,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "        2\n",
-            "0.0137 x - 0.3686 x + 2.596\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_r2_score(df_after_2006, model_after_2006))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-6-r63AIXswO",
-        "outputId": "7c34cee2-aec7-4bd4-fbf0-a544fafd8ebd"
-      },
-      "execution_count": 40,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9298091200395427\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_coorelation(df_after_2006))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ACIjE-vNGX0r",
-        "outputId": "ad9e5325-2195-4f5a-e5bb-704b19a46f82"
-      },
-      "execution_count": 41,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9617973838637672\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "> **Regression result after 2006**\n",
-        "\n",
-        "| Variable      | Coefficient |\n",
-        "| ----------- | ----------- |\n",
-        "| (log GDP per capita)^2      | ß0 = 0.0137       |\n",
-        "| log GDP per capita   | ß1 = -0.3686        |\n",
-        "| R-squared      | 0.9298091200395427       |\n",
-        "| α   | 2.596        |\n",
-        "\n",
-        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
-        "\n",
-        "\n",
-        "> **Coorelation result after 2006**\n",
-        "\n",
-        "Coorelation between FLFP and log GDP per capita: **0.9617973838637672**\n",
-        "\n",
-        "*This shows strong coorelation*"
-      ],
-      "metadata": {
-        "id": "5F-3vmjWg6ZQ"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# **CUMMULATIVE**"
-      ],
-      "metadata": {
-        "id": "qsabvQSrZmr2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_scatter_plot(df)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "RMyEgx93ZsIb",
-        "outputId": "f93f195a-04f7-4b75-cf99-29df99d3c7ed"
-      },
-      "execution_count": 42,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "draw_reg_plot(df)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "z4xOmx13ZzOr",
-        "outputId": "62429a1f-700c-4c6d-d78d-501e2835c4dd"
-      },
-      "execution_count": 43,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "cummulative_model = draw_extended_reg_plot(df)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "UQrJDemBZ7HL",
-        "outputId": "25c68472-0d2c-416a-8f06-3907d4680cb5"
-      },
-      "execution_count": 44,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(cummulative_model)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5AQvx5_waII-",
-        "outputId": "5580c86d-363c-494c-f044-9653a756f8da"
-      },
-      "execution_count": 45,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "          2\n",
-            "-0.02601 x + 0.4626 x - 1.745\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_r2_score(df, cummulative_model))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "qhRE4vgSaP56",
-        "outputId": "8d20854f-6a45-461c-9eed-70b3c60447f7"
-      },
-      "execution_count": 46,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9538936281313893\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(get_coorelation(df))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "qtl3hIpmGvaY",
-        "outputId": "e2600a18-815c-41b6-bf46-1adbf38afdfc"
-      },
-      "execution_count": 47,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9020312937686403\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "> **Regression result (cummulative)**\n",
-        "\n",
-        "| Variable      | Coefficient |\n",
-        "| ----------- | ----------- |\n",
-        "| (log GDP per capita)^2      | ß0 = -0.02601       |\n",
-        "| log GDP per capita   | ß1 = 0.4626        |\n",
-        "| R-squared      | 0.9538936281313893       |\n",
-        "| α   | -1.745        |\n",
-        "\n",
-        "*R square value is above 0.7 so it implies quadratic fit is a fit for the data.*\n",
-        "\n",
-        "\n",
-        "> **Coorelation (cummulative)**\n",
-        "\n",
-        "Coorelation between FLFP and log GDP per capita: **0.9020312937686403**\n",
-        "\n",
-        "*This shows strong coorelation*"
-      ],
-      "metadata": {
-        "id": "_KaWc-2nhwVt"
-      }
-    }
-  ]
-}
\ No newline at end of file