{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab 08 ##\n",
    "\n",
    "How does intelligence and education impact the level of income?\n",
    "\n",
    "In order to estimate a regression, import the statsmodels module (https://www.statsmodels.org):\n",
    "\n",
    "`\n",
    "import statsmodels.api as sm\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "model.summary()\n",
    "`\n",
    "* y may be a series with data corresponding to the target (or dependent variable)\n",
    "* X may be a dataframe with data corresponding to the features (or independent variable)\n",
    "\n",
    "Note: Information related to IQ level is not validated. Data were obtained from the Internet. On the other hand, IQ is culturally biased, and values correspond to average\n",
    "\n",
    "dataFile='https://github.com/masterfloss/data/blob/main/exerciseInt.xlsx?raw=true'\n",
    "\n",
    "1. Read data and analyse it.\n",
    "\n",
    "2. Create a regression, where y is the Income, and all the others are features of the model.\n",
    "\n",
    "3. Analise output\n",
    "\n",
    "4. Create another repression, where y is the Income. IQ and 'Education expenditure per capita' are features of the model.\n",
    "\n",
    "5. Analyse relationship between Income and each one of the features using skatter plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}