Dept Data Mocks for English.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# imprting the dataset\n",
    "OGdataset = pd.read_csv('mockData.csv')\n",
    "dataset=OGdataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "##  Drop the columns where all elements are nan:\n",
    "dataset = dataset.dropna(axis=1, how='all')\n",
    "dataset = dataset.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#still cannot get rid of NANs from rows so interim use the following\n",
    "dataset = dataset.iloc[:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname Forename</th>\n",
       "      <th>Edexcel En P1 Sec A - S3 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P1 Sec A - S6 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P1 Sec A - R Y11 Mock 2</th>\n",
       "      <th>Edexcel Gd En P1 Sec A Y11 Mock 2</th>\n",
       "      <th>Edexcel En P1 Sec B AO5.1 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P1 Sec B AO6.1 Y11 Mock 2</th>\n",
       "      <th>Attain KS4 En - GCSE P1 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec A - S Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec A - R1 Y11 Mock 2</th>\n",
       "      <th>...</th>\n",
       "      <th>Edexcel En P2 Sec A - C6 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec A - C14 Y11 Mock 2</th>\n",
       "      <th>Edexcel Gd En P2 Sec A Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec B AO5.1 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec B AO6.1 Y11 Mock 2</th>\n",
       "      <th>Edexcel En P2 Sec B AO5.2 Y11 Mock 2</th>\n",
       "      <th>Attain KS4 En - GCSE P2 Y11 Mock 2</th>\n",
       "      <th>Attainment KS4 En - New G Y11 Mock 2</th>\n",
       "      <th>Apply6thFrm</th>\n",
       "      <th>Apply6thFrm_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pupil 1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4+</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5-</td>\n",
       "      <td>4-</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pupil 2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1+</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pupil 3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6-</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pupil 4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2+</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2+</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pupil 5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5-</td>\n",
       "      <td>4-</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Surname Forename  Edexcel En P1 Sec A - S3 Y11 Mock 2  \\\n",
       "0          Pupil 1                                  3.0   \n",
       "1          Pupil 2                                  3.0   \n",
       "2          Pupil 3                                  3.0   \n",
       "3          Pupil 4                                  3.0   \n",
       "4          Pupil 5                                  3.0   \n",
       "\n",
       "   Edexcel En P1 Sec A - S6 Y11 Mock 2  Edexcel En P1 Sec A - R Y11 Mock 2  \\\n",
       "0                                  3.0                                 8.0   \n",
       "1                                  0.0                                 0.0   \n",
       "2                                  4.0                                10.0   \n",
       "3                                  2.0                                 3.0   \n",
       "4                                  3.0                                 5.0   \n",
       "\n",
       "   Edexcel Gd En P1 Sec A Y11 Mock 2  Edexcel En P1 Sec B AO5.1 Y11 Mock 2  \\\n",
       "0                                3.0                                  11.0   \n",
       "1                                1.0                                   9.0   \n",
       "2                                4.0                                  14.0   \n",
       "3                                2.0                                   3.0   \n",
       "4                                3.0                                  12.0   \n",
       "\n",
       "   Edexcel En P1 Sec B AO6.1 Y11 Mock 2 Attain KS4 En - GCSE P1 Y11 Mock 2  \\\n",
       "0                                   7.0                                 4+   \n",
       "1                                   4.0                                  2   \n",
       "2                                   9.0                                 6-   \n",
       "3                                   5.0                                 2+   \n",
       "4                                   9.0                                  4   \n",
       "\n",
       "   Edexcel En P2 Sec A - S Y11 Mock 2  Edexcel En P2 Sec A - R1 Y11 Mock 2  \\\n",
       "0                                 4.0                                  7.0   \n",
       "1                                 3.0                                  0.0   \n",
       "2                                 5.0                                  4.0   \n",
       "3                                 3.0                                  5.0   \n",
       "4                                 6.0                                  6.0   \n",
       "\n",
       "        ...        Edexcel En P2 Sec A - C6 Y11 Mock 2  \\\n",
       "0       ...                                        4.0   \n",
       "1       ...                                        0.0   \n",
       "2       ...                                        4.0   \n",
       "3       ...                                        0.0   \n",
       "4       ...                                        2.0   \n",
       "\n",
       "   Edexcel En P2 Sec A - C14 Y11 Mock 2  Edexcel Gd En P2 Sec A Y11 Mock 2  \\\n",
       "0                                   4.0                                2.0   \n",
       "1                                   0.0                                1.0   \n",
       "2                                   7.0                                3.0   \n",
       "3                                   0.0                                2.0   \n",
       "4                                   6.0                                3.0   \n",
       "\n",
       "   Edexcel En P2 Sec B AO5.1 Y11 Mock 2  Edexcel En P2 Sec B AO6.1 Y11 Mock 2  \\\n",
       "0                                  17.0                                  11.0   \n",
       "1                                   8.0                                   5.0   \n",
       "2                                  13.0                                   8.0   \n",
       "3                                  13.0                                   8.0   \n",
       "4                                  15.0                                   9.0   \n",
       "\n",
       "   Edexcel En P2 Sec B AO5.2 Y11 Mock 2  Attain KS4 En - GCSE P2 Y11 Mock 2  \\\n",
       "0                                   NaN                                  5-   \n",
       "1                                   NaN                                   2   \n",
       "2                                   NaN                                   4   \n",
       "3                                   NaN                                   3   \n",
       "4                                   NaN                                  5-   \n",
       "\n",
       "  Attainment KS4 En - New G Y11 Mock 2 Apply6thFrm Apply6thFrm_cat  \n",
       "0                                   4-           y               1  \n",
       "1                                   1+           n               0  \n",
       "2                                    4           y               1  \n",
       "3                                   2+           n               0  \n",
       "4                                   4-           n               0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### creating dummy data\n",
    "# X=pd.get_dummies(dataset['Apply6thFrm'])\n",
    "\n",
    "# sixthFrm=dataset.Apply6thFrm.astype(\"category\").cat.codes\n",
    "# type(sixthFrm)\n",
    "\n",
    "dataset[\"Apply6thFrm\"] = dataset[\"Apply6thFrm\"].astype('category')\n",
    "dataset.dtypes\n",
    "dataset[\"Apply6thFrm_cat\"] = dataset[\"Apply6thFrm\"].cat.codes\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  custom function scaling the the raw marks into percent % for uniformity in comparison\n",
    "def scaleValue(value, OldMax, OldMin, NewMax, NewMin):\n",
    "    OldRange = (OldMax - OldMin)  \n",
    "    NewRange = (NewMax - NewMin)  \n",
    "    NewValue = (((value - OldMin) * NewRange) / OldRange) + NewMin\n",
    "    \n",
    "    return NewValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming the features to improve understanding and output\n",
    "datasetCon=dataset.rename(index=str, columns={\"Edexcel En P1 Sec A - S3 Y11 Mock 2\": \"P1:Q1-M3\",\n",
    "                                              \"Edexcel En P1 Sec A - S6 Y11 Mock 2\": \"P1:Q2-M6\",\n",
    "                                              \"Edexcel En P1 Sec A - R Y11 Mock 2\":\"P1:Q3-M15\",\n",
    "                                              \"Edexcel Gd En P1 Sec A Y11 Mock 2\":\"GD Sec A Paper1\",\n",
    "                                              \"Edexcel En P1 Sec B AO5.1 Y11 Mock 2\":\"P1:Q1-M24\",\n",
    "                                              \"Edexcel En P1 Sec B AO6.1 Y11 Mock 2\":\"P1:Q2-M16\",\n",
    "                                              \"Attain KS4 En - GCSE P1 Y11 Mock 2\":\"Grade: Paper 1\",\n",
    "                                              \"Edexcel En P2 Sec A - S Y11 Mock 2\":\"P2:Q1-M6\",\n",
    "                                              \"Edexcel En P2 Sec A - R1 Y11 Mock 2\":\"P2:Q2-M15\",\n",
    "                                              \"Edexcel En P2 Sec A - R2 Y11 Mock 2\":\"P2 Q3:-M15\",\n",
    "                                              \"Edexcel En P2 Sec A - C6 Y11 Mock 2\":\"P2 Q4:M6\",\n",
    "                                              \"Edexcel En P2 Sec A - C14 Y11 Mock 2\":\"P2 Q5:-M14\",\n",
    "                                              \"Edexcel Gd En P2 Sec A Y11 Mock 2\":\"Paper 2 Secion A Grade\",\n",
    "                                              \"Edexcel En P2 Sec B AO5.1 Y11 Mock 2\":\"P2 Sec B Q1-M24\",\n",
    "                                              \"Edexcel En P2 Sec B AO6.1 Y11 Mock 2\":\"P2 Sec B Q2-M16\",\n",
    "                                              \"Attain KS4 En - GCSE P2 Y11 Mock 2\":\"Grade: Paper 2\",\n",
    "                                              \"Attainment KS4 En - New G Y11 Mock 2\":\"OVERALL GRADE\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Demetrios\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "# converting 4+,4,4- to floats to be mapped against 9-1 grading\n",
    "# this means adding .075 to the 5+ -> 5.75\n",
    "for i in range(0, len(datasetCon['Grade: Paper 1'])):\n",
    "    if ('+') in datasetCon['Grade: Paper 1'].iloc[i]:\n",
    "        datasetCon['Grade: Paper 1'].iloc[i] = int(datasetCon['Grade: Paper 1'].iloc[i][0]) + 0.75\n",
    "    elif ('-') in datasetCon['Grade: Paper 1'].iloc[i]:\n",
    "        datasetCon['Grade: Paper 1'].iloc[i] = int(datasetCon['Grade: Paper 1'].iloc[i][0]) + 0.25\n",
    "    else:\n",
    "        datasetCon['Grade: Paper 1'].iloc[i] = int(datasetCon['Grade: Paper 1'].iloc[i])\n",
    "        \n",
    "for i in range(0, len(datasetCon['Grade: Paper 1'])):\n",
    "    if ('+') in datasetCon['Grade: Paper 2'].iloc[i]:\n",
    "        datasetCon['Grade: Paper 2'].iloc[i] = int(datasetCon['Grade: Paper 2'].iloc[i][0]) + 0.75\n",
    "    elif ('-') in datasetCon['Grade: Paper 2'].iloc[i]:\n",
    "        datasetCon['Grade: Paper 2'].iloc[i] = int(datasetCon['Grade: Paper 2'].iloc[i][0]) + 0.25\n",
    "    else:\n",
    "        datasetCon['Grade: Paper 2'].iloc[i] = int(datasetCon['Grade: Paper 2'].iloc[i])\n",
    "        \n",
    "for i in range(0, len(datasetCon['OVERALL GRADE'])):\n",
    "    if ('+') in datasetCon['OVERALL GRADE'].iloc[i]:\n",
    "        datasetCon['OVERALL GRADE'].iloc[i] = int(datasetCon['OVERALL GRADE'].iloc[i][0]) + 0.75\n",
    "    elif ('-') in datasetCon['OVERALL GRADE'].iloc[i]:\n",
    "        datasetCon['OVERALL GRADE'].iloc[i] = int(datasetCon['OVERALL GRADE'].iloc[i][0]) + 0.25\n",
    "    else:\n",
    "        datasetCon['OVERALL GRADE'].iloc[i] = int(datasetCon['OVERALL GRADE'].iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasetCon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presenting findings:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare overall Paper 1 v Paper 2 v Overall Grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a boxplot based on 3 features to compare\n",
    "\n",
    "gradeBreakdown=[\"Grade: Paper 1\",\"Grade: Paper 2\",\"OVERALL GRADE\"]\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "bplot = sns.boxplot(\n",
    "                 data=datasetCon[gradeBreakdown],\n",
    "                 width=0.5,\n",
    "                 palette=\"colorblind\").set_title(\"PAPER 1   +  PAPER 2  + OVERALL GRADE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grade: Paper 1</th>\n",
       "      <th>Grade: Paper 2</th>\n",
       "      <th>OVERALL GRADE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Grade: Paper 1  Grade: Paper 2  OVERALL GRADE\n",
       "count             78.0            78.0          78.00\n",
       "unique            22.0            17.0          18.00\n",
       "top                5.0             3.0           4.25\n",
       "freq              10.0             9.0          15.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCon[[\"Grade: Paper 1\",\"Grade: Paper 2\",\"OVERALL GRADE\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a scatter plot of Paper 1 vs Overall grade\n",
    "og=datasetCon['Grade: Paper 1']\n",
    "y_col=datasetCon['OVERALL GRADE']\n",
    "plt.scatter(og,y_col,s=60, c='red', marker='^') \n",
    "\n",
    "plt.title(\"Grade: Paper 1 vs. OVERALL GRADE \")\n",
    "plt.xlabel('Grade: Paper 1')\n",
    "plt.ylabel('OVERALL GRADE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/IP85uEz8KhU8HmgNoezOJ1xFX19ocnKBwMBaH2rRHHJz4dG+HQyv8isC0Jw9D7RpY1WoTm2BsGzZMvTo0QNTpkzBX3/9hdGjR2Pr1q36i/7WxBYI8o4fkP8c5B4/IP85uEr8PonJUOp0xolYq0VVUgrK4mfU+lhL5hBg5kpd4qhRKNq6w6pYndoCoUmTJvrLiAUEBECj0RhdR5SIyBVV780rKiqMbldUVNhlr95wb97ovJB2r16SRD9mzBj88ccfiI6OxujRozF58mT4+PhIcSoiIrvxTl0MaDSm79RobN6rr7k3f9f9Eu3VS7J14+vri4ULF0oxNBGRZDxOn4Lo5w8RAEQRQkkJRH9/QLi9Bvc4ddKm8ZUXcwBBgLl3JJU5F2wa3xyWwxAR/a14+Rr91z6Js+Hz6XyUv/qvOvfmLXXzxFm7jGMtfjKWiKgGqevoHY2JnoioBqnr6B2NiZ6IyEDNyht7Vdw4ExM9EZEBw9V8Nbmv6pnoiYj+JnUdvbMw0RMR/U3qOnpnYXklEdHfjOroTd1vYx29szDRExH9zbCO3p1w64aIyM0x0RMRuTkmeiIiN8dET0Tk5pjoiYjcHBM9EdmdUKRCQHiYZB8wknp8R53DUZjoicjuvFMXw3P/Xsk+YCT1+I46h6Mw0RORXUnd4tcRLYTZppiIqBZSt/h1RAthtikmIjJD6ha/jmghzDbFRES1kLrFryNaCLNNMRGRGVK3+HVEC2G2KSYiqoXULX4d0UKYbYqJiGohdYtfR7QQZptiIqJaSN3i1xEthN21TbEkiT4jIwObNm0CAFRWVuLUqVM4cOAAmjRpIsXpiIioFpIk+qFDh2Lo0KEAgI8++gjh4eFM8kRETiKIomhuO8pmx48fR0pKClauXFnrcTqdDlpt/cNQKhXQanV1H+ii5B4/IP85yD1+QP5zkHv8gOPn4OmptOg4Sffo09LSMGHChDqP02pFqFRl9T5PYKCPTY93NrnHD8h/DnKPH5D/HOQeP+D4OQQH+1t0nGTllcXFxbhw4QK6du0q1SmIiMgCkiX6I0eO4LnnnpNqeCIispBkiT4nJwetWrWSangiuxGKVFAO6C/bTz0CrjcH5cnjaN4iCMqTx50dCkHCRP+vf/0LY8aMkWp4IrvxTl0MYfdu2X7qEXC9OTSJHgZotbf/JqdjCwRq0Nyh77irzUF58jiUlwogAFBeKuCq3gUw0VOD5g59x11tDjVX8VzVOx8TPTVY7tB33NXmYLiaB8BVvYtgoqcGyx36jrvaHMyt3rmqdy4memqQ3KHvuKvNoeZqvhpX9c7HRE8Nkjv0HXe1OdS1aueq3nnYppgapJp9xwWFAFF3p9+SHPqOu9ocFNevA4DZXu7V95PjMdFTg1Sz77gc+6y42hxu5F9z2rmpdty6ISJyc0z0RERujomeiMjNMdETEbk5JnoiIjfHRE9E5OaY6KnBc7Ve7o4gFKkQEB4m2ZylHp+sw0RPDZ6r9XJ3BO/UxfDcv1eyOUs9PlmHiZ4aNFfr5e4IUs+5IT6nro6Jnho0V+vl7ghSz7khPqeuThBF0VxrCodRq7U2fXTb2R/9tpXc4wfkOQehSIVmTz4MoexO3KKPL278dgpiQKATI6sfS74HUs/ZlvHl+BqqydFzCA72t+g4ruipwXK1Xu6OIPWcG+JzKgdc0bsAuccPyG8Oplae1eS6qq/reyD1nG0dX26vIVNkt6I3l/8LCgrqFxGRC3G1Xu6OIPWcG+JzKhdmE/3o0aP1XycnJ+u/fu+996SNiMgBqnu565o2g65pM4jNm9/52s/P6f3ora1DV+TlwuPeYCjycs0eU3POhn/sMWepx6f6M9uP3nBF/8cff5i8vTZpaWnYtWsX1Go1RowYgcjISBvCJLIvV+vlXpNhHXpZ/Iw6j/ePGwcUFsL/zddRtHWHyWNqztnepB6f6s+iN2MNk7sg1Lwi5N2ys7Nx9OhRrF27FitXrsTly5frHyFRA2NtHboiLxeehw9BAOCZfbDWVT01TGYTvWFCtyS5G9q/fz86dOiACRMmYPz48ejdu3e9AyRqaKytQ/ePG2f87zdflyw2kiezVTedO3dG+/btIYoizp07p//6/Pnz+OWXX2oddMaMGbh06RKWLl2K/Px8xMXFYfv27Wb/w9DpdNBq61/8o1QqoNXq6j7QRck9fkD+c3CZ+FUqeDzQ2rgO3dcXmpxcINBExUpuLjzat4PhT5YIQHP2PNCmjeTh2pPLfA9s4Og5eHoqLTrO7B79li1b6n3ywMBAtG3bFo0aNULbtm3RuHFj3Lx5E82aNTN5vFYrsrxSxvED8p+Dq8Tvk5gMpU5nnLi1WlQlpZjcqw8YOdLkOOKoUWb36l2Vq3wPbCG78sqWLVuipKQEGzZswJdffomtW7dCrVajZcuWdQ7auXNn7Nu3D6Io4sqVKygvL0egqdUIEelV780rKiqMbldUVJjcqzfcmzcaB9yrJ2NmE/22bduQkJCAFi1aoGfPnvD19cV///d/Iysrq85B+/Tpg0ceeQQRERGIi4vDBx98AKXSsl8xiBoqa+vQa+7N18S9eqpmdutmxYoVWLVqFXx8fPS3DRkyBHFxcejXr1+dA0+bNs0+ERI1ENV16CIA6HQQSksg+vkDitvrsZp16MqLOYAgwNy7W8qcC2bPpcjLRVBoTxTu2gfd/fbfyxeKVGgyNgbF6Stl9wljd2R2Re/h4WGU5AHAz8+PK3MiiRQvX4Mbp3Nw43QOyl/9F6DVonzsOP1tNevUb544i+tXivR/NJVqo3/fPHHW7Ln848ZBKFJJtupnP3rXYlF5pSGdTt7vihO5Oqn7uUtdd89+9K7H7NbNuXPnMGXKFKPbqssriUg61XX0Au7U0Vvy6VhLmaq7t2eFjtTxk/XM1tEfPnzY7IOeffZZuwbB7pXyjh+Q/xxcJX6p+7kr8nLRtPMTd9Xd3/zPcbvs1bMfvczKK5999lmjP48++ijOnDmDmTNn2itGIqpB6n7u5ip17LVXz370rqnOXjfnzp3DzJkz0bdvX5w9exZJSUmOiIuowbG2jt5aUtfdSx0/1Z/ZRL9jxw7Exsbi/fffx2OPPYaHH34Ys2bNQseOHR0ZH1GDIXU/d6nr7tmP3nWZfTN2+vTpGD16NMaMGYOgoCBkZmY6Mi6iBseojt7U/Tb2c7el7t4SUsdP9Wc20WdmZiIjIwMjR45Ehw4dUFhY6Mi4iBocqfu511ZXbw/sR++6zG7d3HPPPRg/fjx++OEHDB8+HPfffz9CQ0ONrjZFRESuz+yK3lC3bt3QrVs3FBYW4ptvvpE6JiIisiOzK/qCggJ8/PHHWLRoEcrLywEAv//+OzZs2OCw4IiIyHZmV/RTpkzBkCFDcOnSJSxatAienp7IzMxEYmKiI+MjIiIbmU30giBg+PDhAIDQ0FA888wz+O6779C4cWOHBUdERLartXtltcDAQCQlJTHJE+H2B4MCwsMk/QCQ1OdwxBzIdVjUvdLPz8/qC4QTuStHtOCV+hxsI9ywmG1q9vjjj+sv/6dSqYwuBbh//367BsGmZvKOH5D/HCyN37Bpl6XNuqxV33O40hzqQ+6vIcB1m5qZ3aM/ceKE3YIhcheOaMEr9TnYRrjhMbuiB4Cff/4Zzz33HFJSUlBYWAhBEDBt2jS7X+ibK3p5xw/Ifw6WxG9LC15LSd3m1xFzqC+5v4YA113Rm92jT01NxbfffgsA+OWXX/Diiy8iODgYaWlp9omQSGYc0YJX6nOwjXDDZDbRHzx4EPPnzwcANG7cGD179sTEiRORnZ3tsOCIXIUjWvBKfQ62EW64au1HX30h8NGjR+v/7e9v2a8KRO7EES14pT4H2wg3XGbfjFWr1aiqqkKjRo3Qr18/AEBVVRW0Wq3DgiNyBEVeLjweeh6KrL1mL6dn1IJXFCGUlED09wf+Lju2RwteW9r8CkUqKKMGQ0hbZnavnW2EGy6ziT4sLAwJCQl4//33ERAQgOLiYsydOxcvvfSSRQMPHjxYv/pv1aoVWyeQy/KPGwcUFtZ6kWzDFrw+ibPh8+l8lL/6L7tWq9jS5tc7dTGE3btrraBhG+GGq9aqmzVr1mDt2rUoKiqCr68vRo4ciVGjRtU5aGVlJYYPH47NmzdbFASrbuQdPyDfORheLNuSi2S7Yg26K8ZUH3J9DRmSXdUNAERHR2Pr1q3Yu3cvtm3bZlGSB4DTp0+jvLwcY8eORWxsLI4dO2bR44gcrebl9eq6nJ5h1YqrVKu4YkzkWmpd0dfXmTNn8NtvvyEyMhIXL17EuHHjsH37dqP+OYZ0Oh202vqHoVQqoNXq6j7QRck9fkCmc8jNhUf7dkYXyxYBaM6eB9qYWNWrVPB4oLVxDbqvLzQ5uYCdP1tiMVeMqZ5k+RqqwdFz8PRUWnScRRcesVZISAjatGkDQRAQEhKCwMBAXLt2DS1atDB5vFYrcutGxvED8pxDwMiRJm8XR40yuVfvk5gM5d+fKNUfq9WiKinFaZ8sdcWY6kuOr6GaZLl1Y8oPP/xQ5zEbNmxAUlISAODKlSsoLS1FcHCwtacikowiLxeehw+hZqs+AYBn9kEo8nKNb3fBGnRXjIlck9WJPj09vc5jIiIiUFJSghEjRmDy5MmYO3eu2W0bImeouTd/1/019updsQbdFWMi12R19rVkS79Ro0ZYsGBBvQIispUiLxdBoT1RuGuf2Qoa5cUcQBDu1JSLor4mHgCUOReMjnfFGvSaMQkKAaLuToSsi6dqVid69qUnV+cfNw5CkarWuvibJ87qv66uiy+bPFVWNeg1Y3KHPW6ShtlE36NHD5O3q1Tc9yPXZbj3Xr3XXlddvE/a5xBEET5LP0d53FuyrEEnqo3ZRG/vi4sQOYKpunhzq3qAvdmpYbD6zdjqBmdErqZmJY25CppqNatWWK1C7srqRF9aWipFHEQ2M1dJY+7TruzNTg2F1Ymeb8aSK3KHungiqZjdo1+/fv1dt4miiJs3b0oaEFF9WFIXb7hXb0kNOvfqyV2YTfTXrl0zefvQoUMlC4aovu6qi695fx118axBJ3dmNtG/9dZbd9125swZrF69WtKAiOrDsC7eEqxBp4akzg9MabVaZGZmYvXq1bh+/ToiIyMdERcREdlJrVs369evx3fffYdOnTqhqqoK27dvd2RsRERkB2arbvr374+qqips2rQJ8+bNQ5MmTRwZFxER2YnZRD9nzhycOHECo0ePxpo1a6BWqx0ZFxER2YnZRP/iiy8iPT0dCxcuxNWrV5GXl4dJkyZh9+7djoyPiIhsVOcHplq1aoVJkyYhKysLL7/8Mr799ltHxEVERHZiNtFrNBpkZmbi0KFDtw9UKNCxY0d4eno6LDhyDkVeLpq1b222R0xNnjsz4dHIA547My06XnnyOJq3CILy5HFbwnTa+NY+P0TOZjbRv/vuu9ixYwdSU1OxatUq7NmzB6+88goeffRRR8ZHTmDYz90STWKjjf6u8/joYYBWe/tvCUg9vrXPD5GzmS2v/PPPP5GRkYGqqiqEh4fD09MTK1asQLt27RwZHzmYtf3cPXdmQlBX3e4xo66C585MqPv2N3u88uRxKC8VQACgvFQA5cnj0D76hN3il3p8a58fIldgdkXv5+cH4PZlAXU6HdLT05nkGwBT/dxrU3MVX9eqvuYq296rbqnHt/b5IXIFFnWvbNasGQIDedUdd2dtP3ej1fzfxwt/r+pNMVxtVx9fveq2B6nHt/b5IXIVgmjmat/PPfccunXrBlEUcejQIXTr1k1/n70v/K1Wa23qMyL3PiWuEn/AS/3vavUrAlB36WbyKk3NWjY3SvTVx4uejXCj4Ppdxwd1esQoEVcfr/2vlig8dsrm+G0Z35LvgbXPj6O5yuuovuQeP+D4OQQH+1t0nNk9+k8//VT/dVRUlO0RkUuzpJ+74V50zdW84fGm9uprrrYNj7fHXrrU41v7/BC5klr36J999tm7/hQXFzsyPnIQS/q5G6pzL77m3n0de+W27qVLPb61zw+RKzGb6JOSkvRfv/rqq/qvV6xYIW1E5BT6fu4m/kAQ7urnLqirAPy9VVPjj+H91RTXr9d6fPX99SX1+EbPj+H4Zp7/Cg+iAAATyElEQVQfWwlFKgSEh/FKV2QXZrduDLfuNQZX4jGzpX+XGzduYOjQoazWkQlr+7lfv3rnNzufxNnw+XQ+yiZPNXtVphv5pi9kYy+G41fvpdtz79zw+ZFi/Jq8UxfDc/9eXumK7MLsit7w2rDmvjZHrVbjgw8+gJeXl43hkaurvvaqIIouca1VU3XuchofcL3nlOTPbKIXRRFqtRpVVVV3fV2X5ORkREVF4Z577rFrsOR6vFMXQ9TpAACiTgvvJZ85NR6p69wdUUfvas8pyZ/Z8srQ0FAIgqBP7NVfC4KAnTt3mh0wIyMDly9fxptvvomYmBjMnDmzzq0bnU4HrdayLSFTlEoFtFpdvR/vbLKNX6WCxwOtIZTdKScTfX2hyckFnPG5i9xceLRvd1f5o+bseaBN7RUxFn0PbBjfYjY8p7J9Hf1N7vEDjp+Dp6fSouPMJvo///wTrVu3tvrEI0eOhCAIEAQBp06dwgMPPIAlS5YgODjY7GNYRy/P+H0SZ8N7yWIoKir0t+m8vFD+5kSn7CvbUufuKnX0tjyncn0dVZN7/IDr1tGbTfSDBw9GYGAghg8fjhdeeAEeHnVeXvYulq7omejlF79QpEKzJx82WnlWE318ceO3UxADHLeqV+TlomnnJ+6qcwduJ+Ob/zlea517Xd8DW8e3hK3PqRxfR4bkHj/guone7B795s2bMXXqVBw5cgRhYWGYN28ecnP5UW97E4pUUA7oL7s33LxTFwMG1VhGNJpa95WlaPMrdZ27I+robXlOiWpT6zL9sccew2OPPYaqqipkZWUhOTkZlZWV+Prrry0afOXKlXYJ0p15py6GsHu37MroPE6fgujnf7tOvfAmBFG8XVMe1PT2/adOmn2sYZtfe2156Ovczd1vY5271OMDNZ5TU/fX8pwS1cai/ZjCwkLk5+fj+vXrCAkJkTqmBqNmGV153FsO3e6wRfHyNQButx4I6t399paGKKIwY0utrQakavNr7ecAXG184M5zSmRvZrduysvLsXnzZowePRrjx4+Hr68vvvrqKyQnJzsyPrfmDmV01rYFZptfIscz+2Zs9+7dERoaisjISHTs2FHSIBrim7Gm3nhzxpuYtjBazf9NBFC454DJVb2pNzTt9UamreT4GqpJ7nOQe/yADN+MzczMxOzZs9G0aVMcOXIE+fn5dguOjFfz1eS2qje3ejd3u7k3NLmqJ5KW2T16QRAwfvx4qFQqtGzZEhcvXkSzZs3wySef6K8+RfWj35s3qJUGAEVFhWz26q1tC8w2v0TOY3ZFP3/+fAwYMADr1q3DggULsHHjRrzwwgtISUlxZHxuyR3K6KxtC8w2v0TOYzbRnz59GoMHDza6LTIyEmfOnJE8KHdXXUana9oMuqbNIDZvfudrPz9ZlNFZ2xbY2jbIRGQ/ZrduzH0SVqm0rLcCmVezjE6Ob0LVbDtc1xwcUZ5IRKaZXdEHBgbi+HHjiyofP34cAQEBkgdFRET2Y3ZFP23aNMTFxaFLly64//77kZ+fj4MHD2LJkiWOjI+IiGxkdkXfqlUrbNiwAc888wzUajU6duyIb775Bvfff78j4yMiIhvV2gKhcePG+Oc//+moWIiISAJmV/REROQemOiJiNwcE72TKU8eh4ePF5Qnj9d9MKTp5U5E7o2J3smaRA8DNJo6P2lazbCXOxGRJZjonciwX0x1f5jamOrlTkRUFyZ6J2IvdyJyBCZ6J6nZ/bGuVX3N7o9c1RORpZjonYS93InIUZjoncCSXu6GLOnlTkRkDhO9E7CXOxE5EhO9E7h6L3epa/WFIhUCwsMgFKkkGZ+IjNXa66a+tFotZsyYgZycHCiVSiQmJqJ169ZSnEqWDHu5B3V6BMpLBdD+V0sUHjtl8nhH93I3rNUv2rrD7uN7py6G5/698F7yGcriZ9h9fCIyJsmKfvfu3QCAdevWYeLEiUhMTJTiNLJnbR29I0hdq6+/Xq4owmfp51zVEzmAJIm+X79+mD17NgDg0qVLaN68uRSnkT1r6+gdQepafe/UxRB1OgCAqNPK4vq4RHIniKIo1n1Y/UyfPh0//vgjFi1ahB49epg9TqfTQautfxhKpQJara7ej3eK48fh0fkfRpU0IgDNf44CTzzhnJhyc+HRvt3dMZ09D7RpU+tDLfoeqFTweKA1hLI7lxwUfX2hyckFAgPrH7cdyPI1VIPc5yD3+AHHz8HT07JLu0qa6AHg2rVrGDZsGL7//nv4+PiYPEat1tp0zVQ5XnO1em++ZlKtba9eagEv9b+rjFMEoO7Src69eku+Bz6Js+G9ZDEUFRX623ReXih/c6LT9+rl+BqqSe5zkHv8gOPnEBzsb9FxkmzdbN68GWlpaQAAb29vCILAi4obsLaO3hGkrtWv3ps3TPIAoKio4F49kcQkSfT9+/fHyZMnMXLkSLz22mtISEhA48aNpTiVLFlbR+8IUtfqe6cuBjQa03dqNNyrJ5KQJOWVPj4+WLhwoRRDuzyhSIUmY2NQnL4SYoDpfWfDOvra7q/v+PWhr9U3d7+Ntfoep09B9PM3O77HqZM2jU9E5kmS6BsyS2rEDevoq/fFLdkHt3T8+pC6Vr94+RpJxyci8/jJWDuytkbc2pp11qATUX0w0duRtTXi1tasswadiOqDid5OalaV1FVNYm1/eWvHJyKqxkRvJ4ar7Wq1rbqt7S9v7fhERNWY6O3A2hpxa2vWWYNORLZgorcDa2vEra1ZZw06EdmC5ZV2YG2NuLU166xBJyJbMNHbgbU14jVr1uvqj8EadCKyBbduiIjcHBM9EZGbY6InInJzTPRERG6OiZ6IyM0x0RMRuTkmeiIiN8dET0Tk5pjoiYjcHBM9EZGbY6InInJzTPRERG6OiZ6IyM0x0duZIi8Xzdq3rvNC30REjsJEb2f+ceMgFKnqvNA3EZGj2L0fvVqtRkJCAgoKClBVVYW4uDj07dvX3qdxSYaXCKy+JKDu/jbODouIGji7r+i3bNmCwMBArFmzBl9++SVmz55t71O4rJqXCOSqnohcgSCKorkr1NXLrVu3IIoi/Pz8UFhYiIiICOzcubPWx+h0Omi19Q9DqVRAq9XV+/F2kZsLj/btjC74LQLQnD0PtKl9Ve8S8dtI7nOQe/yA/Ocg9/gBx8/B01Np0XF237rx9fUFAJSWlmLixImYNGlSnY/RasVaL6VXl7ouxecIASNHmrxdHDUKRVt31PpYV4jfVnKfg9zjB+Q/B7nHDzh+DsHB/hYdJ8mbsX/99RdiY2PxyiuvICwsTIpTuBTDvXlDhnv1RETOYvdEf/36dYwdOxZTp05FRESEvYd3STX35u+6n3v1ROREdk/0S5cuRXFxMVJTUxETE4OYmBhUVFTY+zR6QpEKygH9IRSpJDtHXZQXcwBBgGjiDwQBypwLdjuXUKRCQHiYU+dLRPJi9z36GTNmYMaMGfYe1izv1MUQdu+G95LPUBbvuPMaunnirMPO5Z26GJ779zp1vkQkL7L+wJRQpIJP2ucQRBE+Sz93+1VuQ5svEdmHrBO9d+piiLrbpUyiTgvvJZ85OSJpNbT5EpF9yDbRV69uFX/v/ysqKtx6ldvQ5ktE9iPbRG+4uq3mzqvchjZfIrIfWSb6mqvbau66ym1o8yUi+5JlovdOXQxoNKbv1GjcbpXb0OZLRPZl9/JKR/A4fQqinz+qu+MICgGi7k6vHI9TJ50TmERqzveu+91svkRkX7JM9MXL1xj92x16ZNSm5nyJiKwhy60bIiKyHBM9EZGbY6InInJzTPRERG6OiZ6IyM3Z/VKCRETkWriiJyJyc0z0RERujomeiMjNMdETEbk5JnoiIjfHRE9E5OaY6ImI3Jwsu1cCgFqtRkJCAgoKClBVVYW4uDj07dvX2WFZRavVYsaMGcjJyYFSqURiYiJat27t7LCsduPGDQwdOhTp6elo166ds8Ox2uDBg+Hv7w8AaNWqFRITE50ckXXS0tKwa9cuqNVqjBgxApGRkc4OySoZGRnYtGkTAKCyshKnTp3CgQMH0KRJEydHZhm1Wo34+HgUFBRAoVBg9uzZLvdzINtEv2XLFgQGBmLevHkoLCzEkCFDZJfod+/eDQBYt24dsrOzkZiYiCVLljg5Kuuo1Wp88MEH8PLycnYo9VJZWQkAWLlypZMjqZ/s7GwcPXoUa9euRXl5OdLT050dktWGDh2KoUOHAgA++ugjhIeHyybJA8BPP/0EjUaDdevW4cCBA/j000+xePFiZ4dlRLZbNwMGDMDbb7+t/7dSqXRiNPXTr18/zJ49GwBw6dIlNG/e3MkRWS85ORlRUVG45557nB1KvZw+fRrl5eUYO3YsYmNjcezYMWeHZJX9+/ejQ4cOmDBhAsaPH4/evXs7O6R6O378OM6dO4fhw4c7OxSrhISEQKvVQqfTobS0FB4errd+dr2ILOTr6wsAKC0txcSJEzFp0iQnR1Q/Hh4emD59On788UcsWrTI2eFYJSMjA02bNkXPnj3xxRdfODucevHy8sJrr72GyMhIXLx4EePGjcP27dtd8ofVlMLCQly6dAlLly5Ffn4+4uLisH37dgiC4OzQrJaWloYJEyY4Owyr+fj4oKCgAAMHDkRhYSGWLl3q7JDuItsVPQD89ddfiI2NxSuvvIKwsDBnh1NvycnJ2LFjB95//32UlcnnSlkbN27Ezz//jJiYGJw6dQrTp0/HtWvXnB2WVUJCQvDyyy9DEASEhIQgMDBQVnMIDAxEjx490KhRI7Rt2xaNGzfGzZs3nR2W1YqLi3HhwgV07drV2aFYbdmyZejRowd27NiB7777DvHx8fotQVch20R//fp1jB07FlOnTkVERISzw6mXzZs3Iy0tDQDg7e0NQRBktQW1evVqrFq1CitXrsQjjzyC5ORkBAcHOzssq2zYsAFJSUkAgCtXrqC0tFRWc+jcuTP27dsHURRx5coVlJeXIzAw0NlhWe3IkSN47rnnnB1GvTRp0kT/Zn5AQAA0Gg20Wq2TozImj99PTVi6dCmKi4uRmpqK1NRUAMCXX34pqzcF+/fvj/feew8jR46ERqNBQkICGjdu7OywGpSIiAi89957GDFiBARBwNy5c2WzbQMAffr0wZEjRxAREQFRFPHBBx/IarFQLScnB61atXJ2GPUyZswYJCQkIDo6Gmq1GpMnT4aPj4+zwzLCNsVERG5Otls3RERkGSZ6IiI3x0RPROTmmOiJiNwcEz0RkZuTTx0ZNTh5eXmYN28eLl++DC8vL3h5eWHq1Klo37691WPt3bsXP/zwg75m3lKhoaFo0aIFFAoFRFFEYGAgkpKS4OfnZ3UMlrp06RISEhKg1WohiiJmzZqFtm3bSnY+cn9M9OSSysvLERcXh9mzZ+Mf//gHAOD333/HrFmzHN6ALD09Xf/5hnnz5iEjIwOxsbGSnW/hwoUYNWoU+vXrh3379uGTTz7BZ599Jtn5yP0x0ZNL2r17N7p27apP8gDQsWNHrFixAgAQHx8PlUoFlUqFJUuWYP78+bh8+TIKCwvx/PPPY9KkSTh//jwSEhLg7e0Nb29vBAQEAAC2bduGZcuWQaFQoHPnznj33Xdx7tw5rFq1CjNnzjQbk06nQ0lJCUJCQlBaWor/+Z//QUlJCQoLCxEZGYno6GjExMQgJCQEOTk5EEUR//u//4vg4GAsWLAAR44cgSiKGDNmDAYOHIiYmBgEBQWhuLgYX3/9tf6DTtOnT9d/0lKr1fJDdGQzJnpySfn5+Ua9+ePi4lBaWoqrV69i+fLlAICuXbtizJgxyM/PR6dOnRAZGYnKykp9ol+4cCEmTpyI7t2744svvsCFCxegUqmwePFibNy4Ed7e3pg6dSoOHDiA7t27m03yY8eOhUKhgCAI6NixIwYPHowzZ85g0KBB6N+/P65cuYKYmBhER0cDAJ566inMmjULq1evRlpaGnr27In8/HysW7cOlZWVGDZsGLp37w4ACAsLwwsvvGB0vqZNmwIALly4gOTkZHz++ef2fnqpgWGiJ5d033334cSJE/p/V/fpHzZsGDQaDYDbDcmA2429jh8/jkOHDsHPzw9VVVUAgLNnz6Jjx44AbiffCxcu4M8//8TNmzfx+uuvAwBu3bqFvLy8WmMx3Lqp1rx5cyxfvhyZmZnw8/PTxwRA35jrqaeewq5du3Dvvffijz/+QExMDABAo9Hg0qVLRnOo6dChQ/joo4+QkpLC/XmyGatuyCX17dsXBw8eNOoPn5ubi8uXL+tb8Fb/nZGRAX9/fyxYsABjx45FRUUFRFFE27ZtcfToUQDQ/6fRqlUrtGjRAunp6Vi5ciVGjRqFJ5980ur40tPT0alTJ8yfPx8DBgyAYSeR6nP9+uuvePDBB9G2bVt06dIFK1euxPLlyzFw4EB9XxdT7YQPHTqEOXPm4KuvvsITTzxhdWxENXFFTy7J19cXS5YswYIFCzB//nxoNBp4eHhg9uzZaNmypdGx3bp1wzvvvIP//Oc/8Pb2Rps2bXD16lV8+OGHmDx5Mr7++ms0bdoUjRs3RtOmTTFmzBjExMRAq9WiZcuWGDhwoEV79Ib69OmDmTNnYuvWrQgMDIRSqdT/JrFp0yYsW7YM3t7eSElJQWBgIA4fPozo6GiUlZWhX79+tVbtzJ07V395OuD2qn/WrFn1eyKJwKZmRHYVExODmTNnutw1Q6lh49YNEZGb44qeiMjNcUVPROTmmOiJiNwcEz0RkZtjoicicnNM9EREbu7/AchO1U8uj7VxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a scatter plot of Paper 2vs Overall grade\n",
    "og=datasetCon['Grade: Paper 2']\n",
    "y_col=datasetCon['OVERALL GRADE']\n",
    "plt.scatter(og,y_col,s=60, c='red', marker='^')\n",
    "\n",
    "plt.title(\"Grade: Paper 2 vs. OVERALL GRADE \")\n",
    "plt.xlabel('Grade: Paper 2')\n",
    "plt.ylabel('OVERALL GRADE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Check for accuracy using zip \n",
    "# for a,b in zip(datasetCon['Grade: Paper 2'],datasetCon['OVERALL GRADE']):\n",
    "#     print(a,\"---\",b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breakdown for each question:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# each question has a different weighting or max mark so\n",
    "# in order to compare each we must scale the marks to a %\n",
    "# we will call and send arguments to the -def scaleValue() function \n",
    "datasetCon['P1:Q1-M3']=datasetCon['P1:Q1-M3'].apply(scaleValue, args=(3,0,100,0))\n",
    "datasetCon['P1:Q2-M6']=datasetCon['P1:Q2-M6'].apply(scaleValue, args=(6,0,100,0))\n",
    "datasetCon['P1:Q3-M15']=datasetCon['P1:Q3-M15'].apply(scaleValue, args=(15,0,100,0))\n",
    "datasetCon['P1:Q1-M24']=datasetCon['P1:Q1-M24'].apply(scaleValue, args=(24,0,100,0))\n",
    "datasetCon['P1:Q2-M16']=datasetCon['P1:Q2-M16'].apply(scaleValue, args=(16,0,100,0))\n",
    "datasetCon['P2:Q1-M6']=datasetCon['P2:Q1-M6'].apply(scaleValue, args=(6,0,100,0))\n",
    "datasetCon['P2:Q2-M15']=datasetCon['P2:Q2-M15'].apply(scaleValue, args=(15,0,100,0))\n",
    "datasetCon['P2 Q4:M6']=datasetCon['P2 Q4:M6'].apply(scaleValue, args=(6,0,100,0))\n",
    "datasetCon['P2 Q5:-M14']=datasetCon['P2 Q5:-M14'].apply(scaleValue, args=(14,0,100,0))\n",
    "datasetCon['P2 Sec B Q1-M24']=datasetCon['P2 Sec B Q1-M24'].apply(scaleValue, args=(24,0,100,0))\n",
    "datasetCon['P2 Sec B Q2-M16']=datasetCon['P2 Sec B Q2-M16'].apply(scaleValue, args=(16,0,100,0))\n",
    "len(datasetCon.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '%Percent')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creatin a boxplot based on a list of features -colsofinterest-\n",
    "# this allows a clear comparison and relational picture to be drawn\n",
    "colsofinterest =['P1:Q1-M3', 'P1:Q2-M6','P1:Q3-M15','P1:Q1-M24','P1:Q2-M16', 'P2:Q1-M6', 'P2:Q2-M15','P2 Q4:M6','P2 Q5:-M14','P2 Sec B Q1-M24','P2 Sec B Q2-M16']\n",
    "q=datasetCon[colsofinterest].boxplot(figsize = (20,13))\n",
    "q.set_title('Breakdown by Question')\n",
    "q.set_xlabel(\"Each Question\")\n",
    "q.set_ylabel('%Percent')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## P2 Q5 M14 -- TIMING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplpot seaborn version so we import seaborn for nicer output\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# MATPLOT Version\n",
    "# datasetCon[colsofinterest].boxplot(figsize = (20,13))\n",
    "\n",
    "# SEABORN VErsion\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "bplot = sns.boxplot(\n",
    "                 data=datasetCon[colsofinterest],\n",
    "                 width=0.5,\n",
    "                 palette=\"colorblind\").set_title(\"Breakdown by each question\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P1:Q1-M3</th>\n",
       "      <th>P1:Q2-M6</th>\n",
       "      <th>P1:Q3-M15</th>\n",
       "      <th>P1:Q1-M24</th>\n",
       "      <th>P1:Q2-M16</th>\n",
       "      <th>P2:Q1-M6</th>\n",
       "      <th>P2:Q2-M15</th>\n",
       "      <th>P2 Q4:M6</th>\n",
       "      <th>P2 Q5:-M14</th>\n",
       "      <th>P2 Sec B Q1-M24</th>\n",
       "      <th>P2 Sec B Q2-M16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.435897</td>\n",
       "      <td>62.820513</td>\n",
       "      <td>57.521368</td>\n",
       "      <td>54.113248</td>\n",
       "      <td>52.403846</td>\n",
       "      <td>83.547009</td>\n",
       "      <td>44.957265</td>\n",
       "      <td>66.452991</td>\n",
       "      <td>41.483516</td>\n",
       "      <td>56.677350</td>\n",
       "      <td>53.285256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.939803</td>\n",
       "      <td>23.250173</td>\n",
       "      <td>23.394699</td>\n",
       "      <td>18.248693</td>\n",
       "      <td>17.365870</td>\n",
       "      <td>17.092734</td>\n",
       "      <td>16.203465</td>\n",
       "      <td>28.362423</td>\n",
       "      <td>23.025174</td>\n",
       "      <td>13.335020</td>\n",
       "      <td>13.276390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>46.666667</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>35.714286</td>\n",
       "      <td>54.166667</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>92.857143</td>\n",
       "      <td>79.166667</td>\n",
       "      <td>81.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         P1:Q1-M3    P1:Q2-M6   P1:Q3-M15  P1:Q1-M24  P1:Q2-M16    P2:Q1-M6  \\\n",
       "count   78.000000   78.000000   78.000000  78.000000  78.000000   78.000000   \n",
       "mean    97.435897   62.820513   57.521368  54.113248  52.403846   83.547009   \n",
       "std      8.939803   23.250173   23.394699  18.248693  17.365870   17.092734   \n",
       "min     66.666667    0.000000    0.000000   0.000000   0.000000   50.000000   \n",
       "25%    100.000000   50.000000   46.666667  45.833333  43.750000   66.666667   \n",
       "50%    100.000000   66.666667   53.333333  50.000000  56.250000   83.333333   \n",
       "75%    100.000000   83.333333   73.333333  62.500000  62.500000  100.000000   \n",
       "max    100.000000  100.000000  100.000000  91.666667  87.500000  100.000000   \n",
       "\n",
       "       P2:Q2-M15    P2 Q4:M6  P2 Q5:-M14  P2 Sec B Q1-M24  P2 Sec B Q2-M16  \n",
       "count  78.000000   78.000000   78.000000        78.000000        78.000000  \n",
       "mean   44.957265   66.452991   41.483516        56.677350        53.285256  \n",
       "std    16.203465   28.362423   23.025174        13.335020        13.276390  \n",
       "min     0.000000    0.000000    0.000000        12.500000        25.000000  \n",
       "25%    33.333333   50.000000   28.571429        50.000000        43.750000  \n",
       "50%    46.666667   66.666667   35.714286        54.166667        50.000000  \n",
       "75%    53.333333   83.333333   57.142857        66.666667        62.500000  \n",
       "max    80.000000  100.000000   92.857143        79.166667        81.250000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a tabular breakdown for each question\n",
    "datasetCon[colsofinterest].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing writing Questions from Paper 1 vs. Paper 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Demetrios\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\Demetrios\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Paper 1 Evaluation</th>\n",
       "      <th>Paper 2 Evaluation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.791667</td>\n",
       "      <td>69.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.250000</td>\n",
       "      <td>32.291667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Paper 1 Evaluation  Paper 2 Evaluation\n",
       "0           44.791667           69.791667\n",
       "1           31.250000           32.291667"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing student performance between evaluation of paper 1 + 2\n",
    "\n",
    "# since the evaluate section of each paper is based on 2 questions\n",
    "# first we create a copy dataset \n",
    "# we add Q1+2 of section B together\n",
    "# and we add 2 new columns to the datastrucure which are a calculation of existing columns\n",
    "# finally we are printing \n",
    "\n",
    "colsofinterestEvaluate =['P1:Q1-M24','P1:Q2-M16','P2 Sec B Q1-M24','P2 Sec B Q2-M16']\n",
    "\n",
    "datasetEval=datasetCon[colsofinterestEvaluate]\n",
    "# these are appended to the end of datasetEval\n",
    "datasetEval['Paper 1 Evaluation'] = (datasetEval[\"P1:Q1-M24\"] + datasetEval[\"P1:Q2-M16\"])/2\n",
    "datasetEval['Paper 2 Evaluation'] = (datasetEval[\"P2 Sec B Q1-M24\"] + datasetEval[\"P2 Sec B Q2-M16\"])/2\n",
    "\n",
    "# now we just print the new columns of interest\n",
    "datasetEval.iloc[:2,4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Paper 1 Evaluation</th>\n",
       "      <th>Paper 2 Evaluation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.258547</td>\n",
       "      <td>54.981303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.126502</td>\n",
       "      <td>12.653111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.791667</td>\n",
       "      <td>46.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.083333</td>\n",
       "      <td>52.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.500000</td>\n",
       "      <td>63.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.583333</td>\n",
       "      <td>80.208333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Paper 1 Evaluation  Paper 2 Evaluation\n",
       "count           78.000000           78.000000\n",
       "mean            53.258547           54.981303\n",
       "std             17.126502           12.653111\n",
       "min              0.000000           29.166667\n",
       "25%             44.791667           46.875000\n",
       "50%             52.083333           52.083333\n",
       "75%             62.500000           63.281250\n",
       "max             89.583333           80.208333"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing student performance between evaluation of paper 1 + 2\n",
    "datasetEval[['Paper 1 Evaluation','Paper 2 Evaluation']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Evaluation: Paper 1 v Paper 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kwpUDq3whH5YobZCXHAPi2OZP5MM4eMk+SsuTkuJh8SfyQhy8ZB9beZK7/V1JnxuLP5EX4uAl+1jLk6XF1aXt2zwSj9xY/Im8EAcv2cdaniwtrq5NneeReOTGG74qwXzUYi7MqTUfkc0aQaqj/AlJwsWiqx6IyDV4w5eIyAqL7ewtWsgbiIew+BORKllqfzdkLPJQRPJi8SciVbLU/i5GjfZ0aLJwywjf7Oxs7N69GwBQVVWF77//Hlu2bEFmZia0Wi369OmDKVOmuOPQRER2qxqecNvN1gYWnutr3HLlP2zYMGzZsgVbtmzB3XffjXnz5iE9PR0rV67E9u3bcfz4ceTleXZ5NyJHKGVeFiJnubXZ59tvv8VPP/2ERx99FNXV1WjZsiUkSUKfPn1w5MgRdx6ayOUs9QvnLwDyRm6d2G39+vVISkpCeXk5QkNDTdtDQkJw7tzt81vcTKuVEB4e7PCxtVqNU6/3NcxHLUdz4bdkYZ39whsuWYgG48e6JjgP4LlhTi35cFvx//XXX3Hq1Cncd999KC8vh06nMz2m0+kQFhZm9fUGg2A/fxdiPmo5motISxcs5855dW55bpjz9nx4vJ9/bm4uevfuDQAIDQ2Fv78/fvnlFwghcOjQIfTo0cNdhyZyC6XNE0PkDLcV/9OnTyMqqvbLsmDBAsycORMjRoxA586d8Yc//MFdhyZyC0fmieEvAFIqTu+gEsxHLWdyEZi1EyGZC6ApyIfxzijo5qajaniCLPO0uwvPDXPeng97m324khdRPdTVLxxQ1jztRPbgCF8iF1DSPO1kjvdi6sbiT+QCSpqnnWrxXoxlLP5ELqCkedqplqU5+0MyF3goIuVgmz+Ri1i6H0Cew3sxlvHKn0hmzrRBs/26fngvxjIWfyIZOdMGzfbr+uO9GMtY/Ilk5EwbNNuv64/3Yixjmz+RjJxpg2b7tWN4L6ZuvPInkpEzbdBsvyZXYvEnkpEzbdBsvyZXYvEnkpEzbdBsvyZX4sRuKsF81GIuzDEf5rw9Hy6dz//SpUs4f/686R8R0c04/sD72OztM3/+fBw4cABNmzaFEAKSJGHHjh1yxEZEXuDG+IMb3VBvjD8AwCYpBbNZ/L/55hvs378fGg1vDxDR7ayNP2DxVy6bFb1Vq1aoqqqSIxYi8kIcf+CdbF75X7hwAQMGDECrVq0AgM0+RCpiaeWymxnvjKpzFTOOP1A2m8V/5cqVcsRBRApjb1u+bm662fMAjj/wBjabfbRaLZYuXYqJEydi8eLFUGjPUCJyMXvnEuL4A+9k88p/3rx5GDVqFHr27IkvvvgCc+fOxZtvvilHbETkQfVpy+f8Od7H5pV/VVUVHnzwQYSFheGhhx5CTU2NHHERkYfVZy4h9vP3PjaLv8FgwMmTJwEAJ0+ehCRJbg+KiDzP3rmEuM6Ad7Kr2efFF19EcXExmjVrhoyMDDniIiIPu9GMY6u3D/v5eyfO7aMSzEct5sKcs/mIbNYIUh1lREgSLhZddSY0j/D288PpuX2mTp0KAOjTp89t/4jIcb7WPu5L6wwEZu2EX9ton/lsrLHY7PPqq68CAN555x00b97ctP3nn392f1REPsoX58HxlX7+vvjZWGPxyv/HH3/EwYMHMWnSJBw+fBiHDh3CgQMHMH36dDnjI/IpvrgOr6/08/fFz8Yai1f+v/76K/7973/j0qVL2LNnD4DrUzuMHj1atuCIfI2vzoPjC/38ffWzscRi8e/Rowd69OiBvLw83H333XLGROSzOA+Ocqnts7HZz7+wsBDjx4/HmDFjkJiYiCFDhsgRF5FP4jq8yqW2z8Zm8X/ttdcwZcoUNG/eHH/+85/RoUMHu3a8fv16jBw5EsOGDcM777yDs2fPYtSoURg9ejTS09NhNBqdDp7I2/hK+7gvuvHZiJYtVfHZ2Cz+jRs3xh//+EcAwLBhw1BYWGhzp0ePHsVXX32F7du3Y8uWLSgsLMSSJUuQnJyMbdu2QQiBnJwc56Mn8kJVwxNw+VgeLhZdxeVjeT5bXLxR1fAE1Px0ShWfjc3i7+/vj9zcXNTU1ODgwYMoKSmxudNDhw6hffv2SEpKwqRJk9C/f3/k5eUhJiYGABAbG4vPPvvM+eiJPEiO/vruOMat+5S2b3NBpORtbE7vsGDBApw6dQqTJ0/G6tWrTYO/rCktLcX58+exbt065OfnY/Lkyab1fwEgJCQEZWVlVveh1UoIDw+2823U9XqNU6/3NcxHLVfkQtq+DdoZUyFVXB8Jqs0/h4YzpiI4OABilGt6xLnjGHXtU0yehMavw2Vxezu1fFdsFn+DwWBaxWvOnDl27TQ8PBzR0dEICAhAdHQ0AgMDzZqLdDodwsLCbBxXcHoHF2I+arkiFxFz55oK6A1SRQWkuXNR+vDjTu3bnceQI25v5+3fFXund7BZ/KdNmwZJkmA0GpGfn49WrVph+/btVl/TvXt3/POf/8S4ceNQXFyMyspK9OrVC0ePHsW9996LAwcO4L777rPvnRApkBx9wt1xDLX1ZSfLbLb5v/3229ixYwd27tyJffv2oVmzZjZ3OmDAAHTq1AkjRozA5MmTkZaWhpSUFKxZswYjR46EXq/HoEGDXPIGyDt5+/w2csxnY+sYjuTQl+bhIefUa1ZPIQSGDx+O7Oxsd8YEgLN6upqS8nHrHCrA9f7UcnWrc0Uu5HgP1o4BwKHj17nP4GCUrXzVp3u21IeSviuOcFmzz8iRIyFJEoQQuHz5Mnr16uV0cKRuvjD/u71z3bvrGBHd7nYoh3XtU2Rmoort/apj88q/oKDA9HNgYCAiIyPdHhTAK39XU1I+PD3/u5Jy4ShX5tAX8uFK3p4Pp6/8V65caXHJRs7sSc5Q2xwq7mArh4FZO936V8mtbj1e1cBBCPzoA9mOT/VnsfhHR0fLGQepiK/M/+5J1nIo97z0dR2vwaYNuHHp6Ovz4nsrm80+NTU1+Pbbb1FTUwMhBIqLizF48GC3B8ZmH9dSWj7kvjK9mdJy4ShLOYzodnedfxUYolrg8rG827Y7mw9Lx7P3+Erj7eeHvc0+Nov/pEmToNfrUVxcDIPBgKZNm2Lz5s2uiNEqFn/XYj5q+Xou6ns/wF1r+Np7fKXx9vPD6TV8bygvL8fGjRtxzz33IDs7G1VVVU4HR0TOs9TPX+6+/Pbu1533dLx93Ign2Cz+Wq0WAFBZWYmgoCDo9Xq3B0VE1t1oZ9fmn4MkhKldPTBrp+zz0td5vFue487jW8sFWWaz+P/pT3/C2rVr0bFjRyQkJCA0NFSOuIjICltjJeRcM6Cu41WOe1a246tt7V1Xsdnmf/nyZURERAAATp48iVatWiEoKMjtgbHN37WYj1q+kAv286/l6nEj3p4Pl43wHT9+PFq2bImEhATcf//9TgdGRM7jWIlazIVjbDb77N69G88++yz279+PYcOG4fXXX5cjLiKyQm3rzVrDXDjGZvEHgHbt2qFr165o3Lgx/vvf/7o7JiKygWsB12IuHGOzzX/OnDk4fvw4Bg0ahOHDhyMqSp4/pdjm71rMRy3mwhzzYc7b8+Gyfv4DBw7Enj178Pzzz8tW+Ins4em+3d56fK7hS4CV4r9+/XoAwAMPPICjR4+atqensx2NPM/Tfbu99fh1vU47eRL7xKuQxeJ/+PBh08833+Q9ffq0eyMisoOn+3Z76/HrfF1FBfvEq5DF4n/zrYB6LPZFJAtPr0Xrrcf3dNykHBaL/81z+Vua15/IUzy9Fq1cx3f1/D2ezhsph8XiX1RUZFq8/eafi4uL5YyPqE6e7tstx/HdMX9Pna8LDmafeBXSzp8/f35dD1y9ehUVFRWoqKhAhw4dTD+3b98eMTExbg/MaBS4ds3xSeSCgvyder2v8bV8GDrfDUPLVvA7/hWksjIYo1qgPHOpXX27XZELZ45vr0ZPJUBz+ZLZNqmmBn7Hv0L56r85dPy64jauehnljw13Wdzeztu/KyEhgXY9z2Y/f09hP3/XYj5qeUsu5Frr2FvyIRdvz4fL+vmTb/J0H3VfF5IyHZHNGyOyaRgimzdGSMp0ux67mTPt8/X5fDXPTbErHvItNid2I98j9xqvahOSMt1sDVsYDGiwaYPpcUuP6ZauMtuPo2sd1+fzDUmZDo2d8ZBvsdrs88knnyAwMBC9e/c2bdu/fz8eeughtwfGZh/Xujkf9V3j1de4+9yIbN4YksFw23bx28JIlh67eKH0tu2OrHVcn8/XWqx1xaMG3l47nJ7Sef78+SgrK0NNTQ02b96MtWvXIiAgAP/85z9lKf7kPuzr7WZ1FFOr2608VjU8od5/jdXr83UkVvIJFtv8T548iZUrV2L16tXo27cvkpOTAXDAly9gX283++0Kv87t1h6rJ5eMAXBhPORdLBZ/g8GA6upqAEBiYiJatWqFRYsWyRYYuY+n+8j7usox425fw/a37dYeqw9XjQFwVTzkfSwW/zFjxmDw4MG4fPkyAGDWrFm4du0avvzyS9mCI/fg/OfupVu6CpXjnoXQaiFwvf28ctyz0C1dZfWx+nDVGr66patg/Mskp+Mh72P1hm9VVRUCAgLMpnc4ceIEOnfu7PbAeMPXtZiPWr6QC67h6z7eng+X9PMPDAy8bV4fOQo/kVJZ66Mv59gJW+369YlF2r7N5XFzHInyWZzewVmPP/44/vWvf2H37t3Izc1FZGQknnvuOWRnZ6OwsNDmFBGc3sG1mI9ajubC1H9fCEgAJCHg9/UxSBdLoPn1KhpOfw6ay5cgAdD8+isCPv4IhpatYOh8t8vfgzGyCQI+/ghSTY1pm2jQAOWZS+H3fZ7dsQRm7UTQ1CRoLrku7hv3I+TKhat5+3fFZdM7bNy4EePHj6/XwauqqjBy5Ei8++67pm1Dhw7FmjVr0KJFC0ycOBHJycm4+27LJwKbfVyL+ajlaC6s9Yk3Nv+d7GMnLI0BqE8/f3eM+fD2cSTe/l2xt9nHZvEfM2YMNm3aBG09un4dP34cs2bNwp133omamho899xzSEtLw969ewEAb775JvR6PZ599lmL+zAajTAYHO9WqtVqYDAYHX69r2E+ajmaC78AP9Q1ubkAAEmy2AZfUyXvVaRfoL/dsdTnue44vhJ5+3fF39++Wm1zeofS0lL07dsXUVFRkCQJkiRhx44dVl8TFBSE8ePHIz4+HmfOnMGECRMQFhZmejwkJATnzt1+ZXAzg0Hwyt+FfCUfjox4vZXDV/5abd2Dn6xc+RvvjLrtWK54D9b2E3FnlN2x1Oe59nLHPuXk7d8Vp0f43rBu3bp6H7x169Zo1aoVJElC69at0bBhQ1y5csX0uE6nM/tlQGQPT89JVDlmnPm8PDDvE1/XY1UDB5ntw1Xvwdp+6jMnkG5uOhrOmAqposLmc+3l6JxEJC+bs3r6+fnh5ZdfRlpaGg4fPoyLFy/a3OmuXbvw0ksvAbi+KExlZSWCg4Pxyy+/QAiBQ4cOoUePHs5HT6ri6XVzrfXRD/zog9uahCQAgR99YLbNVe/BVf38q4YnwPD6OpeO+eA4Eu9gs81/4sSJGDduHP72t79hwYIFmD17NnbutN5tq7q6GnPmzMH58+chSRJmzpwJjUaDxYsXw2AwoE+fPpg2bZrVffCGr2v5Qj5c1bfdHbmwNzZXvQf283cfb8+Hy5p9qqqq0KtXL7z++uuIjo5GYKDtbkQBAQFYuXLlbdtt/dIgssZopS3Z0+yNzVXvQcm5IO9gs9knICAABw8ehNFoxNdff42AgAA54iK6jZLnJLI3Nle9ByXngryDzUFeMTEx2LBhAwoKCnD27FnMnTsXjRo1cntgHOTlWr6QD1etm+uOXNgbm6vegyvXEPaFc8OVvD0fLl3D98SJEzh9+jTatm2LDh06OB2cPdjm71rMRy3mwhzzYc7b8+GyNXxffvllLFy4EMePH0d6ejo2bNhg6yVEsrh1/piQlOleMZ8M570hJbB5w/fgwYPYtWsXNBoNDAYDRo4caXVkLpEc6urnfnM/e6WuS+zpsQpEN9i88r/jjjug0+kAADU1NYiMjHR7UES21NnP/ZbnyDkGwF6eHqtAdIPNK//i4mIMGjQIHTt2xE8//QR/f3888cQTAGCCd8w3AAAQfUlEQVRzmgcid7F3vWGlrUvM9ZNJKWwW/9WrV8sRB1G9WOrnXtfz6uvmOXNEeGNAAqTSUqfm4bk5njrj1mgQ2ayRS45BZA+bxb+mpgb79u2DXn+961NxcTEWLlzo9sCIrKlz/hiYN/040u/91jZ5qfSy6TFXtM9bjPu3CeN4D4DkYrPNPyUlBQBw7Ngx5Ofnm03QRuQpdc0fUznuWafnk6mrTf5mzrbP3xq30Gq94l4F+R6bV/5BQUH4y1/+gjNnzmDJkiUYPXq0HHER2VQ1POG24q5b6tw+7Wl7d7Z9/ua4I5vVPWCS9wDI3Wxe+QshUFJSgoqKClRUVODq1fpNGiW3G32o/QL92Yea6s2uewS/tc/X5/yy1Lff1lq8RO5is/hPmTIFH330ER577DE8+OCDiI2NlSMuh9xor9Xmn4MkhKn9lL8AyF51zZlzsxvt8/U5v6ydl5yjhzzF6vQO5eXl0Gq1aGDly+Aujkzv4O1rh7qTtw9ZdyVbubDU2wcaTZ1r+No6v2ydl65a2ctRPDfMeXs+nF7D96233sI//vEP+Pn5ITU1FX379nVpgLY4UvxdOce5r/H2E9qVHF7G0cHzS+nnJc8Nc96eD6fn9tmzZw/27duHHTt24M0333RZYO7E9lNyJ0fPL56XpEQWi39AQAACAgIQERFh6uOvdGw/JXdy9PzieUlKZPOGL3C9x4834Nqh5E6Onl88L0mJLLb59+7dG7169YIQAp9//jl69epleqyuJRpdjfP5uxbzUYu5MMd8mPP2fDi9hu8rr7xi+vnGRG5E3sLTPWiIlM5i8Y+JiZEzDiKX4Zz5RLbZ1eZP5E04Zz6RbSz+5HM4Zz6RbSz+5HPYr57INhZ/8jnsV09kG4s/+Rz2qyeyzeZ8/kTeqK65/omoFq/8iYhUiMWfnGJpkRJPU2pcRErBZh9ymFIHUyk1LiIlcduV/6VLl9CvXz/8/PPPOHv2LEaNGoXRo0cjPT0dRqPRXYclGSl1MJVS4yJSErcUf71ej7S0NAQFBQEAlixZguTkZGzbtg1CCOTk5LjjsCQzpQ6mUmpcREriluK/dOlSPPHEE2jatCkAIC8vzzRXUGxsLD777DN3HJZkptTBVEqN6wbejyAlcHmbf3Z2NiIiItC3b1/8/e9/B3B9PQBJkgAAISEhKCsrs7kfrVZCeHiww3FotRqnXu9r3JEPkZkJMXkSpIra6W9FcDBEZqZHc28rLk+eG9L2bdDOmGqKTZt/Dg1nTEVwcADEqNEeiYnfFXNqyYfVBdwd8eSTT0KSJEiShO+//x533XUXTpw4gRMnTgAA9u/fj88++wxpaWlW98P5/F3LXflQ6tTJ1uLy5LlhazF3T+B3xZy358Pp+fwdtXXrVtPPiYmJmD9/PpYvX46jR4/i3nvvxYEDB3Dfffe5+rDkIUodTKXUuHg/gpRCln7+KSkpWLNmDUaOHAm9Xo9BgwbJcVgixVH6/QhSD7f289+yZYvp57feesudhyLyCrq56WZjEABOOkeewRG+RDLipHOkFBzhSyQzpd6PIHXhlT+5DfuzEykXr/zJLTi/DpGy8cqf3ILz6xApG4s/uQX7sxMpG4s/uYUz/dl5r4B8iVLPZxZ/cgtHF1G/ca9Am38OkhCmewVK+cIQ1YeSz2cWf3ILR/uz814B+RIln8/s7UNu40h/dt4rIF+i5POZV/7kEZbaQTn3DfkSJZ/PLP4kO2vtoI7eKyBSIiWfzyz+JDtr7aCc+4Z8iZLPZ5cv5uIqXMzFtZSUj8hmjSDVcdoJScLFoqtuP76ScqEEzIc5b8+HvYu58MqfZKfkdlAitWDxJ9kpuR2USC1Y/El2Sm4HJVIL9vMnj+Cc9kSexSt/IiIVYvEnIlIhFn8iIhVi8SciUiEWf/IIpc5xTqQW7O1DsuP6vkSexyt/kp2S5zgnUgsWf5Kdkuc4J1ILFn+Sna/M7cP7FuTNWPxJdr4wt4+S12YlsgeLP8nOF+b24X0L8nbs7UMe4e1z+/C+BXk7XvmTR3h7e7mv3Lcg9XLLlb/BYMC8efNw+vRpaLVaLFmyBEIIzJ49G5IkoV27dkhPT4dGw989auQL/fx1c9PN3gPgffctSN3cUn0/+eQTAMCOHTswdepULFmyBEuWLEFycjK2bdsGIQRycnLccWjyAr7QXu4L9y1I3dxy5f/QQw+hf//+AIDz588jMjIS//nPfxATEwMAiI2NxeHDhzFw4EB3HJ4Uzlfay739vgWpm9tu+Pr5+SElJQUfffQRXn31VXzyySeQJAkAEBISgrKyMquv12olhIcHO3x8rVbj1Ot9za35kLZvgzZ1HnDuHNCiBQwZiyBGjZYnmBYtgF9+qXO7HJ8Zzw1zzIc5teTDrb19li5dipkzZyIhIQFVVVWm7TqdDmFhYVZfazAIXLlS4fCxw8ODnXq9r7k5H7e2ueOXX6Cd9BeUVVTLciUbOCetzvbysjlpqJLhM+O5YY75MOft+WjSpKFdz3NLm/+7776L9evXAwAaNGgASZLQpUsXHD16FABw4MAB9OjRwx2HJjt4us2d7eVEnicJIYSrd1pRUYE5c+bg4sWLqKmpwYQJE9CmTRukpqZCr9cjOjoaixYtglartbgPvd7AK38Xujkfkc0aQarjYxeShItFV+UOTXY8N8wxH+a8PR/2Xvm7pdknODgYq1evvm37W2+95Y7DUT0Z74yCNv9cnduJSB3Y0V6FfGFuHSJyDou/CrHNnYg4t49KsY86kbrxyp+ISIVY/ImIVIjFn4hIhVj8iYhUiMWfiEiF3DLCl4iIlI1X/kREKsTiT0SkQiz+REQqxOJPRKRCLP5ERCrE4k9EpEIs/kREKuRTs3oajUbMnz8fJ0+eREBAABYtWoRWrVp5OixZ6fV6vPjiiygoKEB1dTUmT56Mtm3bYvbs2ZAkCe3atUN6ejo0GnX93r906RKGDRuGf/zjH/Dz81N1PtavX4+PP/4Yer0eo0aNQkxMjGrzodfrMXv2bBQUFECj0SAjI0M154dPvaP9+/ejuroab7/9NmbMmIGXXnrJ0yHJ7r333kN4eDi2bduGN954AxkZGViyZAmSk5Oxbds2CCGQk5Pj6TBlpdfrkZaWhqCgIABQdT6OHj2Kr776Ctu3b8eWLVtQWFio6nx8+umnqKmpwY4dO5CUlIRXXnlFNfnwqeL/5Zdfom/fvgCArl274rvvvvNwRPKLi4vD888/b/q/VqtFXl4eYmJiAACxsbH47LPPPBWeRyxduhRPPPEEmjZtCgCqzsehQ4fQvn17JCUlYdKkSejfv7+q89G6dWsYDAYYjUaUl5fDz89PNfnwqeJfXl6O0NBQ0/+1Wi1qamo8GJH8QkJCEBoaivLyckydOhXJyckQQkCSJNPjZWVlHo5SPtnZ2YiIiDBdFABQdT5KS0vx3XffYfXq1ViwYAFmzpyp6nwEBwejoKAADz/8MFJTU5GYmKiafPhUm39oaCh0Op3p/0ajEX5+PvUW7XLhwgUkJSVh9OjRGDJkCJYvX256TKfTISwszIPRySsrKwuSJOHIkSP4/vvvkZKSgsuXL5seV1s+wsPDER0djYCAAERHRyMwMBCFhYWmx9WWj82bN6NPnz6YMWMGLly4gKeffhp6vd70uC/nw6eu/Lt164YDBw4AAL7++mu0b9/ewxHJ7+LFi3jmmWfwwgsvYMSIEQCAzp074+jRowCAAwcOoEePHp4MUVZbt27FW2+9hS1btqBTp05YunQpYmNjVZuP7t274+DBgxBCoKioCJWVlejVq5dq8xEWFoaGDRsCABo1aoSamhrVfF98albPG719fvzxRwghsHjxYrRp08bTYclq0aJF2Lt3L6Kjo03b5s6di0WLFkGv1yM6OhqLFi2CVqv1YJSekZiYiPnz50Oj0SA1NVW1+Vi2bBmOHj0KIQSmTZuGqKgo1eZDp9PhxRdfRElJCfR6PcaMGYMuXbqoIh8+VfyJiMg+PtXsQ0RE9mHxJyJSIRZ/IiIVYvEnIlIhFn8iIhVS3wgoUqSjR48iOTkZbdu2BQBUVVVhyJAhSExMdPuxKysrMW7cOGRmZt7WNXjNmjXYs2ePaWoIAOjduzcmT55s9/6zs7Nx6tQpzJw5s15x5ebmomHDhujYsSOmTJmCtWvX1uv1RNaw+JNi3HfffXj55ZcBANXV1YiLi8PQoUPdOsLy22+/RXp6OoqKiiw+Z+zYsRg1apTbYrAkKysLjzzyCDp27MjCTy7H4k+KVF5eDo1GA61Wiy+++MJU/K5du4alS5fC398fzz//PJo0aYKioiLExsZi2rRpuHDhAlJTU1FVVYXAwEBkZGTAYDBg8uTJCA8PR2xsLCZMmGA6TnV1NV577TXMmjWrXvHl5ORg//79WLJkCQDg8ccfx8aNG7F37158+OGHqKmpQcOGDbFmzRrTa/Lz8zF9+nTs3LkTAJCQkIBVq1bBz88P8+fPR1VVFa5cuYKkpCTccccdOHjwIPLy8tC2bVvEx8fj8OHDOHHiBDIyMqDVak3vz2g0YsaMGbjjjjtw7tw5/P73v8eCBQuc/QjIx7H4k2J8/vnnSExMhCRJ8Pf3R2pqKkJCQvC///0Py5cvR7NmzbBu3Trs27cPQ4YMQUFBATZu3IiGDRti9OjRyMvLwxtvvIHExET069cPR44cwYoVKzBt2jSUlJQgKysLAQEBZsfs3r27zbg2b96Mf//736b/35gNc/ny5aioqMBPP/2Eli1bonHjxrhy5Qo2b94MjUaD8ePH49tvv7W5/1OnTmHcuHG49957cezYMaxZswabNm1C37598cgjj+B3v/ud6bnz5s1DZmYmOnXqhP379+Oll17CrFmzcObMGWzcuBENGjTAQw89hJKSEjRp0qQe2Se1YfEnxbi52edmzZo1Q2ZmJoKDg1FUVIRu3boBADp27Ijw8HAAwD333IPTp0/jxx9/xPr167FhwwYIIeDv7w8AiIqKuq3w28tSs8+gQYPw4Ycf4uuvv0Z8fDw0Gg38/f0xffp0BAcHo7Cw0OqssjcG1zdp0gSvv/46du3aBUmSrL6muLgYnTp1AgD07NkTK1euBAC0bNnSNKNtkyZNUFVV5dB7JfVg8SfFmzdvHvbv34/Q0FCkpKSYiubPP/+MyspKBAQE4JtvvsHw4cMRHR2NZ555Bt26dcPPP/+M3NxcAHDLSkwjRoxAeno6SktLkZaWhh9++AH79+/HO++8g8rKSgwbNgw3z54SGBiIS5cuwWAwQKfTIT8/HwCwevVqxMfHo1+/fsjKysLu3bsBAJIk4dbZV5o2bYoffvgBHTt2RG5uLu666y7Tc4nqg8WfFG/o0KFISEhAWFgYIiMjUVxcDACmdv+LFy8iLi4OHTt2REpKiqn9/Nq1a5g7d67Tx7+12ad169ZYuHAhWrRoAQB48MEHodFo0KpVKzRo0ADDhg1DQEAAmjRpYooVuH5Ffv/992PEiBFo2bKlaYnRuLg4ZGZmYv369WjevDlKS0sBAH/4wx+wYsUKREVFmfaxaNEiZGRkQAgBrVaLxYsXO/3+SJ04sRt5pVtvnhJR/XCQFxGRCvHKn4hIhXjlT0SkQiz+REQqxOJPRKRCLP5ERCrE4k9EpEL/D2quAXuOFlSGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising the training data- ACTUAL Data\n",
    "X2=datasetEval.iloc[:,4:5]  # Paper 1 Evaluation\n",
    "y2=datasetEval.iloc[:,5:6]  # Paper 2 Evaluation\n",
    "plt.scatter(X2,y2, color=\"red\")\n",
    "\n",
    "plt.title(\"Paper 1 Eval vs Paper 2 Eval\")\n",
    "plt.xlabel(\"Paper 1 Evaluation \")\n",
    "plt.ylabel(\"Paper 2 Evaluation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot is much better as a comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1badb1b20b8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetEval[['Paper 1 Evaluation','Paper 2 Evaluation']].boxplot(figsize = (14,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6MmijeDMJMAhB3oJ4QwmAPzBHDgCGI8gBwHAEOQAYjiAHAMMR5ABgOIIcAAzXoCAvKyvTsGHDtH//fh04cEDjxo3T+PHjlZ2dLY/Ht3c6AwDUrd4gr6mpUVZWltq3by9JysnJUXp6ul577TVZlqWCggKfFwkAuLB6g/yJJ57QnXfeqS5dukiSiouLNWjQIElSXFycPvjgA99WCACoU53f7Ny4caM6deqkm266Sc8//7wkybIs2Ww2SVJoaKiOHz9ebyMOh00REb67N8ilJCDAIUmcT7Q59E3/qTPIN2zYIJvNpg8//FB79+5VRkaGysvLvcsrKysVHh5ebyNut6WKiqrmVwu5XG5J4nyizaFvtryoqA4NWq/OIH/11Ve9/05LS9O8efOUm5uroqIiDR48WIWFhbrhhhuaVykAoFka/fHDjIwMLV26VGPHjlVNTY0SExN9URcAoIEafPfDvLw8779Xrlzpk2IAAI3HF4IAwHAEOQAYjiAHAMMR5ABgOIIcAAxHkAOA4QhyADAcQQ4AhiPIAcBwBDkAGI4gBwDDEeQAYDiCHAAMR5ADgOEIcgAwHEEOAIYjyAHAcAQ5ABiuwT/1BuDSUlCwRe++u7nB65eU7JckZWZOb1Q7CQlJio//Q6O2wbkIcgAtolOnTv4u4ZJFkAOoVXz8HxgpG4I5cgAwHEEOAIYjyAHAcAQ5ABiOIAcAwxHkAGA4ghwADEeQA4DhCHIAMBxBDgCGI8gBwHAEOQAYjiAHAMPZLMuyfN1ITY1bFRVVvm7GOI2937P0f/d8jo6+qlHbcc9nwDxRUR0atB63sTUM93wGcL46R+Q1NTWaPXu2SktLVV1drUmTJikmJkaZmZmy2Wzq3bu3srOzZbfXPUPDiBwAGq9FRuRvvvmmIiIilJubK6fTqdGjRys2Nlbp6ekaPHiwsrKyVFBQoISEhBYpGgDQeHUOpZOSkvTwww97HzscDhUXF2vQoEGSpLi4OH3wwQe+rRAAUKc6R+ShoaGSpBMnTuihhx5Senq6nnjiCdlsNu/y48eP19uIw2FTRERIC5QLADhfvW92Hjp0SFOmTNH48eOVnJys3Nxc77LKykqFh4fX24jbbTFHDgCN1NA58jqnVn788UdNnDhRf/nLX5SSkiJJ6tu3r4qKiiRJhYWFGjhwYDNLBQA0R52fWlmwYIE2bdqk6Oho73OPPPKIFixYoJqaGkVHR2vBggVyOBx1NsKnVgCg8Ro6IucLQQDQRrXI1AoAoO0jyAHAcAQ5ABiOIAcAwxHkAGA4ghwADEeQA4DhCHIAMBxBDgCGI8gBwHAEOQAYjiAHAMMR5ABgOIIcAAxHkAOA4QhyADAcQQ4AhiPIAcBwBDkAGI4gBwDDEeQAYDiCHAAMR5ADgOEIcgAwHEEOAIYjyAHAcAQ5ABiOIAcAwxHkAGA4ghwADEeQA4DhCHIAMBxBDgCGI8gBwHAEOQAYjiA3THl5mTIzp8npLPd3KQDaiCYFucfjUVZWlsaOHau0tDQdOHCgpevCBaxatVLFxV9q1aqV/i4FQBvRpCDPz89XdXW11qxZoxkzZmjRokUtXRdqUV5epoKCd2RZlvLzNzMqByCpiUH+ySef6KabbpIk9evXT19++WWLFoXarVq1Uh6PR9KZV0WMygFIUkBTNjpx4oTCwsK8jx0Oh1wulwICat+dw2FTRERI0yqE17ZtW+VyuSRJLpdL771XoNmzM/1cFQB/a1KQh4WFqbKy0vvY4/FcMMQlye22VFFR1ZSmcJZhw36vd9/d5P2jOXx4POcVuIhFRXVo0HpNmlrp37+/CgsLJUm7d+9Wnz59mrIbNNK4cXfLbj9zyex2u8aNu9vPFQFoC5oU5AkJCQoKCtKdd96pnJwc/fWvf23pulCLTp0uU3x8omw2m0aMSFJkZCd/lwSgDbBZlmX5upGaGjdTAC2kvLxMTz65QBkZcwly4CLX0KkVghwA2iifzpEDANoOghwADEeQA4DhCHIAMFyrvNkJAPAdRuQAYDiCHAAMR5ADgOEIcgAwHEEOAIYjyAHAcAQ5ABiuST8scbEqKipSenq6YmJiJEmnT59WcnKy0tLSfN72yZMndd9992nhwoW66qqrzlm2dOlSvf322+rSpYv3uRtvvFGTJk1q8P43btyokpISzZw5s1F17dy5Ux06dFBsbKymTp2qv//9743aHi3DX33z7bff1iuvvCKHw6E+ffpo3rx53nviS/TNtoIgP88NN9ygp556SpJUXV2tpKQk3XrrrQoPD/dZm1988YWys7N15MiRC65z7733aty4cT6r4UI2bNigUaNGKTY29pL+H6UtaO2+eerUKT399NN66623FBwcrOnTp+vf//634uPjz1mPvul/BHkdTpw4IbvdLofDoY8//tjbWU6dOqUnnnhCgYGBevjhhxUVFaUjR44oLi5O06ZN06FDhzR37lydPn1a7dq10/z58+V2uzVp0iRFREQoLi5ODzzwgLed6upqPfvss5o1a1aj6isoKFB+fr5ycnIkSbfddpteeuklbdq0SVu2bJHL5VKHDh20dOlS7zYHDx7U9OnTtXbtWknSHXfcoSVLliggIEDz5s3T6dOnVVFRoSlTpqhbt27avn27iouLFRMTo9TUVO3YsUN79uzR/Pnz5XA4vMfn8Xg0Y8YMdevWTd9//72uvfZaPfroo829BLiA1uibQUFBWr16tYKDgyWd+Z3Ydu3aNag++mbrIsjP89FHHyktLU02m02BgYGaO3euQkND9Z///Ee5ubnq2rWrnnvuOW3evFnJyckqLS3VSy+9pA4dOmj8+PEqLi7WCy+8oLS0NA0bNkwffvihFi9erGnTpunYsWPasGGDgoKCzmlzwIAB9da1YsUK/etf//I+fvDBBzV8+HDl5uaqqqpKX3/9tXr06KHIyEhVVFRoxYoVstvtuv/++/XFF1/Uu/+SkhLdd999Gjx4sHbt2qWlS5fqn//8p2666SaNGjVKv/rVr7zrzpkzRwsXLtTVV1+t/Px8LVq0SLNmzdK3336rl156ScHBwRoxYoSOHTumqKioRpx91KW1+6bdblfnzp0lSXl5eaqqqtLQoUN/URd90/8I8vOc/fL1bF27dtXChQsVEhKiI0eOqH///pKk2NhYRURESJKuu+46ffPNN/rqq6+0fPlyvfjii7IsS4GBgZKk7t27/yLEG+pCL18TExO1ZcsW7d69W6mpqbLb7QoMDNT06dMVEhKiw4cPy+VyXXC/P99qJyoqSv/4xz+0fv162Wy2Orc5evSorr76aknSb3/7W/3tb3+TJPXo0UNhYWHe/Z0+fbpJx4ra+aNvejwe5ebm6ptvvtHSpUtls9l+sQ590/8I8gaaM2eO8vPzFRYWpoyMDG8n279/v06ePKmgoCB9/vnnuv322xUdHa2JEyeqf//+2r9/v3bu3ClJ57xJ1FJSUlKUnZ0tp9OprKws7du3T/n5+Vq3bp1OnjypMWPG6Oz7orVr105lZWVyu92qrKzUwYMHJUnPPPOMUlNTNWzYMG3YsEGvv/66JMlms+n8+6p16dJF+/btU2xsrHbu3Kkrr7zSuy5any/7ZlZWloKCgrRs2bJG91/6ZushyBvo1ltv1R133KHw8HB17txZR48elSTvXOSPP/6opKQkxcbGKiMjwzund+rUKT3yyCPNbv/8l6+9evXSY489piuuuEKSFB8fL7vdrp49eyo4OFhjxoxRUFCQoqKivLVKZ0YjQ4cOVUpKinr06KGePXtKkpKSkrRw4UItX75cl19+uZxOpyTp+uuv1+LFi9W9e3fvPhYsWKD58+fLsiw5HA49/vjjzT4+NJ2v+mZxcbHWr1+vgQMHasKECZKke+65RwkJCeesR9/0P25j2wznvzkDtBX0zUsLXwgCAMMxIgcAwzEiBwDDEeQAYDiCHAAMR5ADgOEIcgAw3P8An1s4ndJJ6pgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the list to be used to plot which is refinement of the original\n",
    "colsofinterestEvalSum =['Paper 1 Evaluation','Paper 2 Evaluation'] # long var name change \n",
    "sns.set_style(\"darkgrid\")\n",
    "bplot = sns.boxplot(\n",
    "                 data=datasetEval[colsofinterestEvalSum],\n",
    "                 width=0.3,\n",
    "                 palette=\"colorblind\").set_title(\"PAPER 1 Eval   +  PAPER 2 Eval\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logostic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Surname Forename', 'P1:Q1-M3', 'P1:Q2-M6', 'P1:Q3-M15',\n",
       "       'GD Sec A Paper1', 'P1:Q1-M24', 'P1:Q2-M16', 'Grade: Paper 1',\n",
       "       'P2:Q1-M6', 'P2:Q2-M15', 'P2 Q3:-M15', 'P2 Q4:M6', 'P2 Q5:-M14',\n",
       "       'Paper 2 Secion A Grade', 'P2 Sec B Q1-M24', 'P2 Sec B Q2-M16',\n",
       "       'Edexcel En P2 Sec B AO5.2 Y11 Mock 2', 'Grade: Paper 2',\n",
       "       'OVERALL GRADE', 'Apply6thFrm', 'Apply6thFrm_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCon.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "colsofinterestEvalSum =['Paper 1 Evaluation','Paper 2 Evaluation']\n",
    "X=datasetEval[['Paper 1 Evaluation','Paper 2 Evaluation']]\n",
    "y=datasetCon['Apply6thFrm_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "#create obj of this class to work with it\n",
    "#right now we only scale the X feature\n",
    "sc_X = StandardScaler()\n",
    "#fit and transform our training and test set\n",
    "#We recompute X_train to scale\n",
    "X_train = sc_X.fit_transform(X_train)  # we call the object sc_X and the method fit_tranform\n",
    "#you have to fit the object to the training set THEN.. transform it\n",
    "X_test = sc_X.transform(X_test) # dont need to fit the test set to object as its already one to the train set\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Demetrios\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=0, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting logistic regression to the training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n",
       "      dtype=int8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predicting the test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.77      0.80        13\n",
      "           1       0.62      0.71      0.67         7\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        20\n",
      "   macro avg       0.73      0.74      0.73        20\n",
      "weighted avg       0.76      0.75      0.75        20\n",
      "\n",
      "                  Predicted: NO  Predicted: SURVIVED\n",
      "Actual: NOT                  10                    3\n",
      "Actual: SURVIVED              2                    5\n"
     ]
    }
   ],
   "source": [
    "# making Confusion Matrix\n",
    "# EVALUATE IF OUR MODEL LEARNT WELL!! CHECK CORRECT AND INCORRECT PREDS\n",
    "# from sklearn.metrics import confusion_matrix  # we are importing a function not a class!!\n",
    "# cm = confusion_matrix(y_train,y_pred)\n",
    "# cm\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "print(classification_report(y_test, y_pred))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, y_pred), ['Actual: NOT', 'Actual: SURVIVED'], ['Predicted: NO', 'Predicted: SURVIVED'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the TRAINING set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('P1 + P2 Evaluation against 5 GCSEs A-c')\n",
    "plt.xlabel('Paper 1 Evaluation')\n",
    "plt.ylabel('Paper 2 Evaluation')\n",
    "plt.legend()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Surname Forename', 'P1:Q1-M3', 'P1:Q2-M6', 'P1:Q3-M15',\n",
       "       'GD Sec A Paper1', 'P1:Q1-M24', 'P1:Q2-M16', 'Grade: Paper 1',\n",
       "       'P2:Q1-M6', 'P2:Q2-M15', 'P2 Q3:-M15', 'P2 Q4:M6', 'P2 Q5:-M14',\n",
       "       'Paper 2 Secion A Grade', 'P2 Sec B Q1-M24', 'P2 Sec B Q2-M16',\n",
       "       'Edexcel En P2 Sec B AO5.2 Y11 Mock 2', 'Grade: Paper 2',\n",
       "       'OVERALL GRADE', 'Apply6thFrm', 'Apply6thFrm_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCon.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting all the Questions % to the LinearRegression() class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up the test and train set\n",
    "#X = datasetCon[['P1:Q1-M24','P1:Q3-M15']]\n",
    "X = datasetCon.iloc[:,1:6].values\n",
    "y=datasetCon[\"OVERALL GRADE\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creatuing a regressor object to fit the training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# ## 'linear_model'= the library\n",
    "# ## 'LinearRegression = the class from lib'\n",
    "regressor = LinearRegression()  #creating the OBJect of the LinearRegression Class -so we call the LinearRegression Class\n",
    "\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.4546938 , 6.6137535 , 3.48584419, 4.7837119 , 3.32157098,\n",
       "       2.36937353, 4.41495739, 5.89643675, 4.11409485, 4.88374096,\n",
       "       4.59567707, 4.59019358, 5.09928322, 4.22143907, 3.11245603,\n",
       "       4.40215875])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare ypred to ytest as a quick overview\n",
    "# for a,b in zip(y_pred,y_test):\n",
    "#     print(a,\"===\",b,\" Difference: \",b-a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.01359043  0.00930264  0.02820522 -0.00731516  0.03852658]\n",
      "Mean squared error: 0.38\n",
      "Variance score: 0.76\n",
      "[0.37901368]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regressor.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))\n",
    "# Explained variance score: 1 is perfect prediction, 0 is random \n",
    "print('Variance score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "print(mean_squared_error(y_test, y_pred, multioutput='raw_values'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['P1:Q1-M3', 'P1:Q2-M6', 'P1:Q3-M15', 'P1:Q1-M24', 'P1:Q2-M16',\n",
       "       'P2:Q1-M6', 'P2:Q2-M15', 'P2 Q4:M6', 'P2 Q5:-M14', 'P2 Sec B Q1-M24',\n",
       "       'P2 Sec B Q2-M16'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCon[colsofinterest].columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi Linear Regression on all questions for their statistical importance on the Overall Grade -- the Optimal Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.01359043  0.00930264  0.02820522 -0.00731516  0.03852658]\n",
      "Mean squared error: 0.38\n",
      "Variance score: 0.76\n",
      "[0.37901368]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regressor.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))\n",
    "# Explained variance score: 1 is perfect prediction, 0 is random \n",
    "print('Variance score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "print(mean_squared_error(y_test, y_pred, multioutput='raw_values'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.953</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   121.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.69e-39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:15</td>     <th>  Log-Likelihood:    </th> <td> -25.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   75.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    66</td>      <th>  BIC:               </th> <td>   103.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.8437</td> <td>    0.528</td> <td>   -1.599</td> <td> 0.115</td> <td>   -1.897</td> <td>    0.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -0.0013</td> <td>    0.005</td> <td>   -0.257</td> <td> 0.798</td> <td>   -0.012</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0055</td> <td>    0.003</td> <td>    1.721</td> <td> 0.090</td> <td>   -0.001</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0068</td> <td>    0.005</td> <td>    1.420</td> <td> 0.160</td> <td>   -0.003</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0208</td> <td>    0.005</td> <td>    4.426</td> <td> 0.000</td> <td>    0.011</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0122</td> <td>    0.006</td> <td>    2.132</td> <td> 0.037</td> <td>    0.001</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0020</td> <td>    0.003</td> <td>    0.685</td> <td> 0.496</td> <td>   -0.004</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0111</td> <td>    0.004</td> <td>    2.596</td> <td> 0.012</td> <td>    0.003</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>    0.0032</td> <td>    0.002</td> <td>    1.676</td> <td> 0.098</td> <td>   -0.001</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>    0.0128</td> <td>    0.003</td> <td>    4.676</td> <td> 0.000</td> <td>    0.007</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>    0.0103</td> <td>    0.006</td> <td>    1.857</td> <td> 0.068</td> <td>   -0.001</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>    0.0205</td> <td>    0.007</td> <td>    3.005</td> <td> 0.004</td> <td>    0.007</td> <td>    0.034</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.199</td> <th>  Durbin-Watson:     </th> <td>   2.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.333</td> <th>  Jarque-Bera (JB):  </th> <td>   1.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.183</td> <th>  Prob(JB):          </th> <td>   0.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.380</td> <th>  Cond. No.          </th> <td>2.72e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.72e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.953\n",
       "Model:                            OLS   Adj. R-squared:                  0.945\n",
       "Method:                 Least Squares   F-statistic:                     121.0\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           2.69e-39\n",
       "Time:                        19:33:15   Log-Likelihood:                -25.592\n",
       "No. Observations:                  78   AIC:                             75.18\n",
       "Df Residuals:                      66   BIC:                             103.5\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.8437      0.528     -1.599      0.115      -1.897       0.210\n",
       "x1            -0.0013      0.005     -0.257      0.798      -0.012       0.009\n",
       "x2             0.0055      0.003      1.721      0.090      -0.001       0.012\n",
       "x3             0.0068      0.005      1.420      0.160      -0.003       0.016\n",
       "x4             0.0208      0.005      4.426      0.000       0.011       0.030\n",
       "x5             0.0122      0.006      2.132      0.037       0.001       0.024\n",
       "x6             0.0020      0.003      0.685      0.496      -0.004       0.008\n",
       "x7             0.0111      0.004      2.596      0.012       0.003       0.020\n",
       "x8             0.0032      0.002      1.676      0.098      -0.001       0.007\n",
       "x9             0.0128      0.003      4.676      0.000       0.007       0.018\n",
       "x10            0.0103      0.006      1.857      0.068      -0.001       0.021\n",
       "x11            0.0205      0.007      3.005      0.004       0.007       0.034\n",
       "==============================================================================\n",
       "Omnibus:                        2.199   Durbin-Watson:                   2.029\n",
       "Prob(Omnibus):                  0.333   Jarque-Bera (JB):                1.686\n",
       "Skew:                          -0.183   Prob(JB):                        0.430\n",
       "Kurtosis:                       2.380   Cond. No.                     2.72e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.72e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the stats model and includinf a constant\n",
    "# this is to ensure \n",
    "import statsmodels.api as smm\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "X = smm.add_constant(datasetCon[colsofinterest])\n",
    "X = np.array(X)\n",
    "X = X.astype(np.float64)\n",
    "Y = np.array(datasetCon['OVERALL GRADE'].values)\n",
    "Y = Y.astype(np.float64)\n",
    "\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.953</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   135.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.33e-40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:16</td>     <th>  Log-Likelihood:    </th> <td> -25.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   73.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    67</td>      <th>  BIC:               </th> <td>   99.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.9611</td> <td>    0.262</td> <td>   -3.662</td> <td> 0.000</td> <td>   -1.485</td> <td>   -0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0054</td> <td>    0.003</td> <td>    1.718</td> <td> 0.090</td> <td>   -0.001</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0071</td> <td>    0.005</td> <td>    1.566</td> <td> 0.122</td> <td>   -0.002</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0207</td> <td>    0.005</td> <td>    4.450</td> <td> 0.000</td> <td>    0.011</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0120</td> <td>    0.006</td> <td>    2.132</td> <td> 0.037</td> <td>    0.001</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0020</td> <td>    0.003</td> <td>    0.709</td> <td> 0.481</td> <td>   -0.004</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0109</td> <td>    0.004</td> <td>    2.612</td> <td> 0.011</td> <td>    0.003</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0032</td> <td>    0.002</td> <td>    1.669</td> <td> 0.100</td> <td>   -0.001</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>    0.0130</td> <td>    0.003</td> <td>    4.815</td> <td> 0.000</td> <td>    0.008</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>    0.0103</td> <td>    0.005</td> <td>    1.875</td> <td> 0.065</td> <td>   -0.001</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>    0.0203</td> <td>    0.007</td> <td>    3.018</td> <td> 0.004</td> <td>    0.007</td> <td>    0.034</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.152</td> <th>  Durbin-Watson:     </th> <td>   2.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.341</td> <th>  Jarque-Bera (JB):  </th> <td>   1.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.184</td> <th>  Prob(JB):          </th> <td>   0.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.385</td> <th>  Cond. No.          </th> <td>1.22e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.22e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.953\n",
       "Model:                            OLS   Adj. R-squared:                  0.946\n",
       "Method:                 Least Squares   F-statistic:                     135.0\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           2.33e-40\n",
       "Time:                        19:33:16   Log-Likelihood:                -25.631\n",
       "No. Observations:                  78   AIC:                             73.26\n",
       "Df Residuals:                      67   BIC:                             99.19\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.9611      0.262     -3.662      0.000      -1.485      -0.437\n",
       "x1             0.0054      0.003      1.718      0.090      -0.001       0.012\n",
       "x2             0.0071      0.005      1.566      0.122      -0.002       0.016\n",
       "x3             0.0207      0.005      4.450      0.000       0.011       0.030\n",
       "x4             0.0120      0.006      2.132      0.037       0.001       0.023\n",
       "x5             0.0020      0.003      0.709      0.481      -0.004       0.008\n",
       "x6             0.0109      0.004      2.612      0.011       0.003       0.019\n",
       "x7             0.0032      0.002      1.669      0.100      -0.001       0.007\n",
       "x8             0.0130      0.003      4.815      0.000       0.008       0.018\n",
       "x9             0.0103      0.005      1.875      0.065      -0.001       0.021\n",
       "x10            0.0203      0.007      3.018      0.004       0.007       0.034\n",
       "==============================================================================\n",
       "Omnibus:                        2.152   Durbin-Watson:                   2.035\n",
       "Prob(Omnibus):                  0.341   Jarque-Bera (JB):                1.669\n",
       "Skew:                          -0.184   Prob(JB):                        0.434\n",
       "Kurtosis:                       2.385   Cond. No.                     1.22e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.22e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,3,4,5,6,7,8,9,10,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   151.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>2.35e-41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:16</td>     <th>  Log-Likelihood:    </th> <td> -25.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   71.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    68</td>      <th>  BIC:               </th> <td>   95.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.8393</td> <td>    0.198</td> <td>   -4.243</td> <td> 0.000</td> <td>   -1.234</td> <td>   -0.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0054</td> <td>    0.003</td> <td>    1.716</td> <td> 0.091</td> <td>   -0.001</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0073</td> <td>    0.005</td> <td>    1.619</td> <td> 0.110</td> <td>   -0.002</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0201</td> <td>    0.005</td> <td>    4.410</td> <td> 0.000</td> <td>    0.011</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0129</td> <td>    0.005</td> <td>    2.365</td> <td> 0.021</td> <td>    0.002</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0117</td> <td>    0.004</td> <td>    2.930</td> <td> 0.005</td> <td>    0.004</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0032</td> <td>    0.002</td> <td>    1.698</td> <td> 0.094</td> <td>   -0.001</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0128</td> <td>    0.003</td> <td>    4.791</td> <td> 0.000</td> <td>    0.007</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>    0.0105</td> <td>    0.005</td> <td>    1.917</td> <td> 0.059</td> <td>   -0.000</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>    0.0199</td> <td>    0.007</td> <td>    2.980</td> <td> 0.004</td> <td>    0.007</td> <td>    0.033</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.767</td> <th>  Durbin-Watson:     </th> <td>   2.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.251</td> <th>  Jarque-Bera (JB):  </th> <td>   2.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.253</td> <th>  Prob(JB):          </th> <td>   0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.367</td> <th>  Cond. No.          </th> <td>    828.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.952\n",
       "Model:                            OLS   Adj. R-squared:                  0.946\n",
       "Method:                 Least Squares   F-statistic:                     151.0\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           2.35e-41\n",
       "Time:                        19:33:16   Log-Likelihood:                -25.923\n",
       "No. Observations:                  78   AIC:                             71.85\n",
       "Df Residuals:                      68   BIC:                             95.41\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.8393      0.198     -4.243      0.000      -1.234      -0.445\n",
       "x1             0.0054      0.003      1.716      0.091      -0.001       0.012\n",
       "x2             0.0073      0.005      1.619      0.110      -0.002       0.016\n",
       "x3             0.0201      0.005      4.410      0.000       0.011       0.029\n",
       "x4             0.0129      0.005      2.365      0.021       0.002       0.024\n",
       "x5             0.0117      0.004      2.930      0.005       0.004       0.020\n",
       "x6             0.0032      0.002      1.698      0.094      -0.001       0.007\n",
       "x7             0.0128      0.003      4.791      0.000       0.007       0.018\n",
       "x8             0.0105      0.005      1.917      0.059      -0.000       0.021\n",
       "x9             0.0199      0.007      2.980      0.004       0.007       0.033\n",
       "==============================================================================\n",
       "Omnibus:                        2.767   Durbin-Watson:                   2.004\n",
       "Prob(Omnibus):                  0.251   Jarque-Bera (JB):                2.134\n",
       "Skew:                          -0.253   Prob(JB):                        0.344\n",
       "Kurtosis:                       2.367   Cond. No.                         828.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,3,4,5,7,8,9,10,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   165.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>6.33e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:17</td>     <th>  Log-Likelihood:    </th> <td> -27.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   72.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    69</td>      <th>  BIC:               </th> <td>   94.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.9039</td> <td>    0.196</td> <td>   -4.612</td> <td> 0.000</td> <td>   -1.295</td> <td>   -0.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0079</td> <td>    0.003</td> <td>    2.860</td> <td> 0.006</td> <td>    0.002</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0219</td> <td>    0.004</td> <td>    4.911</td> <td> 0.000</td> <td>    0.013</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0149</td> <td>    0.005</td> <td>    2.760</td> <td> 0.007</td> <td>    0.004</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0135</td> <td>    0.004</td> <td>    3.474</td> <td> 0.001</td> <td>    0.006</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0037</td> <td>    0.002</td> <td>    1.951</td> <td> 0.055</td> <td>-8.29e-05</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0139</td> <td>    0.003</td> <td>    5.305</td> <td> 0.000</td> <td>    0.009</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0095</td> <td>    0.006</td> <td>    1.730</td> <td> 0.088</td> <td>   -0.001</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td>    0.0204</td> <td>    0.007</td> <td>    3.015</td> <td> 0.004</td> <td>    0.007</td> <td>    0.034</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.591</td> <th>  Durbin-Watson:     </th> <td>   1.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.274</td> <th>  Jarque-Bera (JB):  </th> <td>   1.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.210</td> <th>  Prob(JB):          </th> <td>   0.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.356</td> <th>  Cond. No.          </th> <td>    758.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.951\n",
       "Model:                            OLS   Adj. R-squared:                  0.945\n",
       "Method:                 Least Squares   F-statistic:                     165.7\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           6.33e-42\n",
       "Time:                        19:33:17   Log-Likelihood:                -27.398\n",
       "No. Observations:                  78   AIC:                             72.80\n",
       "Df Residuals:                      69   BIC:                             94.01\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.9039      0.196     -4.612      0.000      -1.295      -0.513\n",
       "x1             0.0079      0.003      2.860      0.006       0.002       0.013\n",
       "x2             0.0219      0.004      4.911      0.000       0.013       0.031\n",
       "x3             0.0149      0.005      2.760      0.007       0.004       0.026\n",
       "x4             0.0135      0.004      3.474      0.001       0.006       0.021\n",
       "x5             0.0037      0.002      1.951      0.055   -8.29e-05       0.007\n",
       "x6             0.0139      0.003      5.305      0.000       0.009       0.019\n",
       "x7             0.0095      0.006      1.730      0.088      -0.001       0.020\n",
       "x8             0.0204      0.007      3.015      0.004       0.007       0.034\n",
       "==============================================================================\n",
       "Omnibus:                        2.591   Durbin-Watson:                   1.941\n",
       "Prob(Omnibus):                  0.274   Jarque-Bera (JB):                1.920\n",
       "Skew:                          -0.210   Prob(JB):                        0.383\n",
       "Kurtosis:                       2.356   Cond. No.                         758.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,4,5,7,8,9,10,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   183.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.93e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:17</td>     <th>  Log-Likelihood:    </th> <td> -29.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   74.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    70</td>      <th>  BIC:               </th> <td>   92.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.7817</td> <td>    0.185</td> <td>   -4.216</td> <td> 0.000</td> <td>   -1.152</td> <td>   -0.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0081</td> <td>    0.003</td> <td>    2.909</td> <td> 0.005</td> <td>    0.003</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0220</td> <td>    0.005</td> <td>    4.851</td> <td> 0.000</td> <td>    0.013</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0144</td> <td>    0.005</td> <td>    2.639</td> <td> 0.010</td> <td>    0.004</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0143</td> <td>    0.004</td> <td>    3.635</td> <td> 0.001</td> <td>    0.006</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0032</td> <td>    0.002</td> <td>    1.685</td> <td> 0.096</td> <td>   -0.001</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0134</td> <td>    0.003</td> <td>    5.068</td> <td> 0.000</td> <td>    0.008</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>    0.0288</td> <td>    0.005</td> <td>    6.001</td> <td> 0.000</td> <td>    0.019</td> <td>    0.038</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.067</td> <th>  Durbin-Watson:     </th> <td>   1.898</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.356</td> <th>  Jarque-Bera (JB):  </th> <td>   1.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.230</td> <th>  Prob(JB):          </th> <td>   0.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.428</td> <th>  Cond. No.          </th> <td>    661.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.948\n",
       "Model:                            OLS   Adj. R-squared:                  0.943\n",
       "Method:                 Least Squares   F-statistic:                     183.7\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           1.93e-42\n",
       "Time:                        19:33:17   Log-Likelihood:                -29.053\n",
       "No. Observations:                  78   AIC:                             74.11\n",
       "Df Residuals:                      70   BIC:                             92.96\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.7817      0.185     -4.216      0.000      -1.152      -0.412\n",
       "x1             0.0081      0.003      2.909      0.005       0.003       0.014\n",
       "x2             0.0220      0.005      4.851      0.000       0.013       0.031\n",
       "x3             0.0144      0.005      2.639      0.010       0.004       0.025\n",
       "x4             0.0143      0.004      3.635      0.001       0.006       0.022\n",
       "x5             0.0032      0.002      1.685      0.096      -0.001       0.007\n",
       "x6             0.0134      0.003      5.068      0.000       0.008       0.019\n",
       "x7             0.0288      0.005      6.001      0.000       0.019       0.038\n",
       "==============================================================================\n",
       "Omnibus:                        2.067   Durbin-Watson:                   1.898\n",
       "Prob(Omnibus):                  0.356   Jarque-Bera (JB):                1.750\n",
       "Skew:                          -0.230   Prob(JB):                        0.417\n",
       "Kurtosis:                       2.428   Cond. No.                         661.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,4,5,7,8,9,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   181.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>5.67e-41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:17</td>     <th>  Log-Likelihood:    </th> <td> -35.795</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   85.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    71</td>      <th>  BIC:               </th> <td>   102.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.7185</td> <td>    0.200</td> <td>   -3.595</td> <td> 0.001</td> <td>   -1.117</td> <td>   -0.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0126</td> <td>    0.003</td> <td>    4.634</td> <td> 0.000</td> <td>    0.007</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0238</td> <td>    0.005</td> <td>    4.876</td> <td> 0.000</td> <td>    0.014</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0163</td> <td>    0.006</td> <td>    2.762</td> <td> 0.007</td> <td>    0.005</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0033</td> <td>    0.002</td> <td>    1.634</td> <td> 0.107</td> <td>   -0.001</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0138</td> <td>    0.003</td> <td>    4.820</td> <td> 0.000</td> <td>    0.008</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>    0.0302</td> <td>    0.005</td> <td>    5.845</td> <td> 0.000</td> <td>    0.020</td> <td>    0.041</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.948</td> <th>  Durbin-Watson:     </th> <td>   2.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.623</td> <th>  Jarque-Bera (JB):  </th> <td>   0.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.157</td> <th>  Prob(JB):          </th> <td>   0.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.222</td> <th>  Cond. No.          </th> <td>    626.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.939\n",
       "Model:                            OLS   Adj. R-squared:                  0.933\n",
       "Method:                 Least Squares   F-statistic:                     181.0\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           5.67e-41\n",
       "Time:                        19:33:17   Log-Likelihood:                -35.795\n",
       "No. Observations:                  78   AIC:                             85.59\n",
       "Df Residuals:                      71   BIC:                             102.1\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.7185      0.200     -3.595      0.001      -1.117      -0.320\n",
       "x1             0.0126      0.003      4.634      0.000       0.007       0.018\n",
       "x2             0.0238      0.005      4.876      0.000       0.014       0.034\n",
       "x3             0.0163      0.006      2.762      0.007       0.005       0.028\n",
       "x4             0.0033      0.002      1.634      0.107      -0.001       0.007\n",
       "x5             0.0138      0.003      4.820      0.000       0.008       0.019\n",
       "x6             0.0302      0.005      5.845      0.000       0.020       0.041\n",
       "==============================================================================\n",
       "Omnibus:                        0.948   Durbin-Watson:                   2.067\n",
       "Prob(Omnibus):                  0.623   Jarque-Bera (JB):                0.481\n",
       "Skew:                          -0.157   Prob(JB):                        0.786\n",
       "Kurtosis:                       3.222   Cond. No.                         626.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,4,5,8,9,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   211.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Apr 2019</td> <th>  Prob (F-statistic):</th> <td>1.36e-41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:33:18</td>     <th>  Log-Likelihood:    </th> <td> -37.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    78</td>      <th>  AIC:               </th> <td>   86.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    72</td>      <th>  BIC:               </th> <td>   100.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.6639</td> <td>    0.199</td> <td>   -3.331</td> <td> 0.001</td> <td>   -1.061</td> <td>   -0.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0133</td> <td>    0.003</td> <td>    4.919</td> <td> 0.000</td> <td>    0.008</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0237</td> <td>    0.005</td> <td>    4.802</td> <td> 0.000</td> <td>    0.014</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.0168</td> <td>    0.006</td> <td>    2.818</td> <td> 0.006</td> <td>    0.005</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>    0.0153</td> <td>    0.003</td> <td>    5.581</td> <td> 0.000</td> <td>    0.010</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>    0.0309</td> <td>    0.005</td> <td>    5.932</td> <td> 0.000</td> <td>    0.021</td> <td>    0.041</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.375</td> <th>  Durbin-Watson:     </th> <td>   2.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.305</td> <th>  Jarque-Bera (JB):  </th> <td>   1.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.241</td> <th>  Prob(JB):          </th> <td>   0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.532</td> <th>  Cond. No.          </th> <td>    538.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.936\n",
       "Model:                            OLS   Adj. R-squared:                  0.932\n",
       "Method:                 Least Squares   F-statistic:                     211.8\n",
       "Date:                Sun, 14 Apr 2019   Prob (F-statistic):           1.36e-41\n",
       "Time:                        19:33:18   Log-Likelihood:                -37.235\n",
       "No. Observations:                  78   AIC:                             86.47\n",
       "Df Residuals:                      72   BIC:                             100.6\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.6639      0.199     -3.331      0.001      -1.061      -0.267\n",
       "x1             0.0133      0.003      4.919      0.000       0.008       0.019\n",
       "x2             0.0237      0.005      4.802      0.000       0.014       0.034\n",
       "x3             0.0168      0.006      2.818      0.006       0.005       0.029\n",
       "x4             0.0153      0.003      5.581      0.000       0.010       0.021\n",
       "x5             0.0309      0.005      5.932      0.000       0.021       0.041\n",
       "==============================================================================\n",
       "Omnibus:                        2.375   Durbin-Watson:                   2.017\n",
       "Prob(Omnibus):                  0.305   Jarque-Bera (JB):                1.674\n",
       "Skew:                          -0.241   Prob(JB):                        0.433\n",
       "Kurtosis:                       3.532   Cond. No.                         538.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X[:,[0,2,4,5,9,11]]\n",
    "regressor_OLS = sm.OLS(endog = Y, exog = X_opt).fit()\n",
    "regressor_OLS.summary()"
   ]
  }
 ],
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