From 3c3ed25e63e1a7152a4319359f6b3e00590e1208 Mon Sep 17 00:00:00 2001 From: test Date: Sat, 14 May 2022 16:47:42 +0200 Subject: [PATCH] delete old function --- bootstrap-t.ipynb | 55 +++++++++++++++-------------------------------- 1 file changed, 17 insertions(+), 38 deletions(-) diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index 7535ea9..338ab8f 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 142, "metadata": { "pycharm": { "name": "#%%\n" @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 143, "metadata": { "pycharm": { "name": "#%%\n" @@ -65,28 +65,7 @@ }, { "cell_type": "code", - "execution_count": 122, - "outputs": [], - "source": [ - "def get_t_stat(data1, data2):\n", - " mean1 = np.mean(data1)\n", - " mean2 = np.mean(data2)\n", - " sem1 = sem(data1)\n", - " sem2 = sem(data2)\n", - "\n", - " sed = sqrt(sem1**2.0 + sem2**2.0)\n", - " return (mean1 - mean2) / sed" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 123, + "execution_count": 144, "outputs": [], "source": [ "def t_stat_single(sample, population_mean):\n", @@ -103,13 +82,13 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 145, "outputs": [], "source": [ - "def t_stat_indept(sample_1, sample_2):\n", + "def t_stat_ind(sample_1, sample_2):\n", " \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek niezależnych\"\"\"\n", - " # get_t_stat() jest ok już chyba dla równolicznych sampli o tej samej wariancji\n", - " return" + " sed = sqrt(sem(sample_1)**2 + sem(sample_2)**2)\n", + " return (mean(sample_1) - mean(sample_2)) / sed" ], "metadata": { "collapsed": false, @@ -120,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 146, "outputs": [], "source": [ "def t_stat_dep(sample_1, sample_2):\n", @@ -139,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 147, "metadata": { "pycharm": { "name": "#%%\n" @@ -150,7 +129,7 @@ "def independent_t_test(data, columns, alpha=0.05):\n", " t_stat_sum = 0\n", " for sample in generate_bootstraps(data):\n", - " t_stat_sum += get_t_stat(sample[columns[0]], sample[columns[1]])\n", + " t_stat_sum += t_stat_ind(sample[columns[0]], sample[columns[1]])\n", "\n", " data_size = data.shape[0]\n", " t_stat = t_stat_sum / data_size\n", @@ -162,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 148, "outputs": [], "source": [ "def make_decision(data, columns, alpha=0.05):\n", @@ -186,12 +165,12 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 149, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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+ "image/png": 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}, "metadata": { "needs_background": "light" @@ -217,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 150, "outputs": [ { "name": "stdout", @@ -248,7 +227,7 @@ "print(t_stat_selfmade, '- z naszej funkcji')\n", "print(t_stat_lib, '- z gotowej biblioteki')\n", "print()\n", - "t_stat_selfmade = get_t_stat(dummy, dummy2)\n", + "t_stat_selfmade = t_stat_ind(dummy, dummy2)\n", "t_stat_lib, _ = ttest_ind(dummy, dummy2)\n", "print('Statystyka testowa dla dwóch prób niezależnych:')\n", "print(t_stat_selfmade, '- z naszej funkcji')\n", @@ -269,13 +248,13 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 151, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "t: 6.89001510574949, df: 998, cv: 1.6463818766348755, p: 9.867218153658541e-12\n", + "t: 6.914346193374633, df: 998, cv: 1.6463818766348755, p: 8.378631122241131e-12\n", "\n", "Reject the null hypothesis that the means are equal.\n", "Reject the null hypothesis that the means are equal.\n"