diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index 770549f..8c2b43d 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 239, "metadata": { "pycharm": { "name": "#%%\n" @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 240, "metadata": { "pycharm": { "name": "#%%\n" @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 241, "outputs": [], "source": [ "def t_stat_single(sample, population_mean):\n", @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 242, "outputs": [], "source": [ "def t_stat_ind(sample_1, sample_2):\n", @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 243, "outputs": [], "source": [ "def t_stat_dep(sample_1, sample_2):\n", @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 244, "outputs": [], "source": [ "def bootstrap_one_sample():\n", @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 245, "outputs": [], "source": [ "def bootstrap_independent():\n", @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 246, "outputs": [], "source": [ "def bootstrap_dependent():\n", @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 247, "metadata": { "pycharm": { "name": "#%%\n" @@ -173,25 +173,28 @@ "source": [ "def independent_t_test(data, columns, alpha=0.05):\n", " t_stat_sum = 0\n", + " t_stat_list = []\n", " for sample in generate_bootstraps(data):\n", - " t_stat_sum += t_stat_ind(sample[columns[0]], sample[columns[1]])\n", - "\n", + " stat = t_stat_ind(sample[columns[0]], sample[columns[1]])\n", + " t_stat_list.append(stat)\n", + " t_stat_sum += stat\n", " data_size = data.shape[0]\n", " t_stat = t_stat_sum / data_size\n", " df = 2 * data_size - 2\n", " cv = t.ppf(1.0 - alpha, df)\n", " p = (1.0 - t.cdf(abs(t_stat), df)) * 2.0\n", - " return t_stat, df, cv, p" + " return t_stat, df, cv, p, t_stat_list" ] }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 248, "outputs": [], "source": [ "def make_decision(data, columns, alpha=0.05):\n", - " t_stat, df, cv, p = independent_t_test(data, columns, alpha)\n", + " t_stat, df, cv, p, stats = independent_t_test(data, columns, alpha)\n", " print(f't: {t_stat}, df: {df}, cv: {cv}, p: {p}\\n')\n", + " draw_distribution(stats)\n", " if abs(t_stat) <= cv:\n", "\t print('Accept null hypothesis that the means are equal.')\n", " else:\n", @@ -210,27 +213,18 @@ }, { "cell_type": "code", - "execution_count": 149, - "outputs": [ - { - "data": { - "text/plain": "
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}, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": 249, + "outputs": [], "source": [ - "def draw_distribution():\n", - " \"\"\"Funkcja rysuje rozkład statystyki testowej\"\"\"\n", - " # Losowe dane bo nie jestem pewien co tu dać teraz\n", - " dummy = np.random.normal(170, 10, 500)\n", - " plt.hist(dummy)\n", - " plt.show()\n", - "draw_distribution() # To trzeba wywalić potem" + "def draw_distribution(stats): # To powinno być zdefiniowane przed make decision w sumie\n", + " \"\"\"\n", + " Funkcja rysuje rozkład statystyki testowej\n", + " stats: lista statystyk testowych\n", + " \"\"\"\n", + " plt.hist(stats)\n", + " plt.xlabel('Test statistic value')\n", + " plt.ylabel('Frequency')\n", + " plt.show()" ], "metadata": { "collapsed": false, @@ -241,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 250, "outputs": [ { "name": "stdout", @@ -293,14 +287,30 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 251, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "t: 6.914346193374633, df: 998, cv: 1.6463818766348755, p: 8.378631122241131e-12\n", - "\n", + "t: 6.929903381575467, df: 998, cv: 1.6463818766348755, p: 7.544853630747639e-12\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Reject the null hypothesis that the means are equal.\n", "Reject the null hypothesis that the means are equal.\n" ]