{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quartet d'Anscombe\n", "\n", "[Page wikipedia](https://fr.wikipedia.org/wiki/Quartet_d%27Anscombe)\n", "\n", "Construits en 1973 par le statisticien Francis Anscombe dans le but de démontrer l'importance de tracer des graphiques avant d'analyser un ensemble de données." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab --no-import-all inline\n", "from scipy.stats import linregress, pearsonr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture des données" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "all_sets = list()\n", "for i in range(0, 8, 2):\n", " x, y = np.loadtxt(\"anscombe.dat\", usecols=(i, i+1), skiprows=1, unpack=True)\n", " all_sets.append((x, y))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10. 8. 13. 9. 11. 14. 6. 4. 12. 7. 5.]\n", "[ 8.04 6.95 7.58 8.81 8.33 9.96 7.24 4.26 10.84 4.82 5.68]\n" ] } ], "source": [ "print(all_sets[0][0])\n", "print(all_sets[0][1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calcul des propriétés statistiques\n", "\n", "Ici on montre que les quatre jeux de données ont les même propriétés statistiques." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def show_stat(data):\n", " x, y = data\n", " print(\"moyenne x : %4.2f\" % x.mean())\n", " print(\"variance x : %4.2f\" % np.var(x))\n", " print(\"moyenne y : %4.2f\" % y.mean())\n", " print(\"variance y : %4.2f\" % np.var(y))\n", " cor, p = pearsonr(x, y)\n", " print(\"corrélation : %5.3f\" % cor)\n", " \n", " a, b, r, p_value, std_err = linregress(x, y)\n", " print(\"regression linéaire : %3.1f x + %3.1f (r^2 = %4.2f)\" % (a, b, r**2))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "set 0\n", "------\n", "moyenne x : 9.00\n", "variance x : 10.00\n", "moyenne y : 7.50\n", "variance y : 3.75\n", "corrélation : 0.816\n", "regression linéaire : 0.5 x + 3.0 (r^2 = 0.67)\n", "\n", "set 1\n", "------\n", "moyenne x : 9.00\n", "variance x : 10.00\n", "moyenne y : 7.50\n", "variance y : 3.75\n", "corrélation : 0.816\n", "regression linéaire : 0.5 x + 3.0 (r^2 = 0.67)\n", "\n", "set 2\n", "------\n", "moyenne x : 9.00\n", "variance x : 10.00\n", "moyenne y : 7.50\n", "variance y : 3.75\n", "corrélation : 0.816\n", "regression linéaire : 0.5 x + 3.0 (r^2 = 0.67)\n", "\n", "set 3\n", "------\n", "moyenne x : 9.00\n", "variance x : 10.00\n", "moyenne y : 7.50\n", "variance y : 3.75\n", "corrélation : 0.817\n", "regression linéaire : 0.5 x + 3.0 (r^2 = 0.67)\n" ] } ], "source": [ "for i, data in enumerate(all_sets):\n", " print(\"\\nset %d\" % i)\n", " print(\"------\")\n", " show_stat(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique des données\n", "\n", "La représentation graphique de ces jeux de données a deux objectifs. Elle montre\n", "\n", "* qu'il est important de visualiser les données pour faire une interprétation\n", "* que des données abérantes peuvent avoir un impact majeur sur certaines propriétés statistique telle que la moyenne." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 8))\n", "fig.suptitle(\"Quartet d'Anscombe\", size=20)\n", "for i, data in enumerate(all_sets):\n", " ax = plt.subplot(2, 2, i + 1)\n", " x, y = data\n", " ax.plot(x, y, marker=\"o\", color=\"C3\", linestyle=\"\", label=\"set %d\" % (i+1))\n", " ax.set_ylabel(\"y%d\" % (i+1), size=14)\n", " ax.set_xlabel(\"x%d\" % (i+1), size=14)\n", " \n", " a, b, r, p_value, std_err = linregress(x, y)\n", " ax.plot([0, 20], [b, a*20 + b], color=\"C0\")\n", " \n", " ax.set_xlim(0, 20)\n", " ax.set_ylim(0, 15)\n", " ax.legend(loc=\"lower right\", fontsize=18)\n", " ax.grid(True)" ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }