From 16c4442ad27d861a5dc6925f6ab82f2925c20cc7 Mon Sep 17 00:00:00 2001 From: s444501 Date: Wed, 18 May 2022 06:37:27 +0200 Subject: [PATCH] test 3 --- bootstrap-t.ipynb | 116 ++++++++++++++++++++++++++++++++-------------- 1 file changed, 81 insertions(+), 35 deletions(-) diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index 50d8ac9..e6eec77 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 510, + "execution_count": 546, "metadata": { "pycharm": { "name": "#%%\n" @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 511, + "execution_count": 547, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 512, + "execution_count": 548, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 513, + "execution_count": 549, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 514, + "execution_count": 550, "metadata": { "pycharm": { "name": "#%%\n" @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 515, + "execution_count": 551, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 516, + "execution_count": 552, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 517, + "execution_count": 553, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 518, + "execution_count": 554, "metadata": {}, "outputs": [], "source": [ @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 519, + "execution_count": 555, "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 556, "metadata": { "pycharm": { "name": "#%%\n" @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 521, + "execution_count": 557, "metadata": { "collapsed": false, "pycharm": { @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 522, + "execution_count": 558, "metadata": { "collapsed": false, "pycharm": { @@ -364,20 +364,9 @@ " return p, t, ts" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test t studenta dla prób zależnych\n", - "\n", - "W odróżnieniu od testu t – Studenta dla prób niezależnych, gdzie porównujemy dwie grupy, ten rodzaj testu stosujemy gdy poddajemy analizie tą samą pojedynczą grupę, ale dwukrotnie w czasie. Na przykład gdy chcemy porównać średnie wagi grupy osób przed dietą oraz po diecie, aby sprawdzić czy dieta spowodowała istotne zmiany statystyczne.\n", - "\n", - "Hipoteza zerowa takiego testu będzie brzmiała H0: Średnia waga osób po diecie jest taka sama jak przed dietą. Hipoteza alternatywna z kolei H1: Dieta znacząco wpłynęła na średnią wagę danej grupy." - ] - }, { "cell_type": "code", - "execution_count": 523, + "execution_count": 559, "metadata": { "collapsed": false, "pycharm": { @@ -399,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 524, + "execution_count": 560, "metadata": { "collapsed": false, "pycharm": { @@ -432,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 525, + "execution_count": 561, "outputs": [], "source": [ "dataset = pd.read_csv('experiment_data.csv')\n", @@ -455,9 +444,37 @@ "\n", "### Hipoteza\n", "\n", - "### Sprawdzenie założeń\n", - "\n", - "## Test" + "### Sprawdzenie założeń\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Test\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 561, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Wniosek" ], "metadata": { "collapsed": false @@ -471,13 +488,42 @@ "### Hipoteza\n", "\n", "### Sprawdzenie założeń\n", - "\n", + "\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ "## Test" ], "metadata": { "collapsed": false } }, + { + "cell_type": "code", + "execution_count": 561, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Wniosek" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "source": [ @@ -516,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 526, + "execution_count": 562, "outputs": [ { "name": "stdout", @@ -557,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 527, + "execution_count": 563, "outputs": [ { "name": "stdout", @@ -571,7 +617,7 @@ "p: 1.0\n", "Wartość statystyki testowej z próby: [7.89079918]\n", "Wartości statystyk z prób boostrapowych:\n", - "[-0.05395381], [0.15520269], [-0.2285374], [1.05735295], [2.77041326], ... (i 95 pozostałych)\n", + "[-2.17000034], [-0.74957325], [-1.53238091], [-2.4791557], [1.17261618], ... (i 95 pozostałych)\n", "\n", "\n" ] @@ -579,7 +625,7 @@ { "data": { "text/plain": "
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\n" + "image/png": 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\n" 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