diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index 08cb6c3..a108827 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" @@ -72,16 +72,7 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = pd.read_csv('experiment_data.csv') # TODO: del?" - ] - }, - { - "cell_type": "code", - "execution_count": 48, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 29, "metadata": { "pycharm": { "name": "#%%\n" @@ -135,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 34, "metadata": { "pycharm": { "name": "#%%\n" @@ -246,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 35, "metadata": { "collapsed": false, "pycharm": { @@ -269,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 36, "metadata": { "collapsed": false, "pycharm": { @@ -291,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 37, "metadata": { "collapsed": false, "pycharm": { @@ -313,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 38, "metadata": { "collapsed": false, "pycharm": { @@ -346,56 +337,22 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 39, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 169.5557\n", - "dtype: float64\n", - "0 175.1417\n", - "dtype: float64\n", - "0 79.6342\n", - "dtype: float64\n", - "0 76.5602\n", - "dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\program files\\python39\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3472: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", - " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" - ] - } - ], + "outputs": [], "source": [ "dataset = pd.read_csv('experiment_data.csv')\n", - "heights_female = pd.DataFrame(dataset['Female height'].to_numpy()) # xd\n", + "heights_female = pd.DataFrame(dataset['Female height'].to_numpy())\n", "heights_male = pd.DataFrame(dataset['Male height'].to_numpy())\n", "weights_before = pd.DataFrame(dataset['Weight before'].to_numpy())\n", - "weights_after = pd.DataFrame(dataset['Weight after'].to_numpy())\n", - "print(np.mean(heights_female))\n", - "print(np.mean(heights_male))\n", - "print(np.mean(weights_before))\n", - "print(np.mean(weights_after))\n" + "weights_after = pd.DataFrame(dataset['Weight after'].to_numpy())\n" ] }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": { @@ -418,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -453,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 41, "metadata": { "collapsed": false, "pycharm": { @@ -470,10 +427,10 @@ "Hipoteza: średnia jest równa 160.0\n", "Hipoteza alternatywna: średnia jest większa\n", "\n", - "p: 0.5\n", + "p: 0.496\n", "Wartość statystyki testowej z próby: [19.1207964]\n", "Wartości statystyk z prób boostrapowych:\n", - "[18.3771515], [18.01787771], [18.0688161], [17.02918795], [17.03895917], ... (i 995 pozostałych)\n", + "[20.14152027], [16.54688877], [18.82250628], [18.76682582], [20.94899879], ... (i 995 pozostałych)\n", "\n", "\n", "\n" @@ -482,7 +439,7 @@ { "data": { "text/plain": "
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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -532,7 +489,38 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 42, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Średni wzrost mężczyzn: 175.14170000000001\n", + "Średni wzrost kobiet: 169.5557\n" + ] + } + ], + "source": [ + "print(f'Średni wzrost mężczyzn: {heights_male.mean()[0]}')\n", + "print(f'Średni wzrost kobiet: {heights_female.mean()[0]}')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -572,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 44, "metadata": { "collapsed": false, "pycharm": { @@ -592,7 +580,7 @@ "p: 0.0\n", "Wartość statystyki testowej z próby: [8.04931557]\n", "Wartości statystyk z prób boostrapowych:\n", - "[0.50661164], [-1.11155681], [1.47250746], [0.52178413], [0.77552826], ... (i 995 pozostałych)\n", + "[-1.88803566], [-0.12474486], [1.30731501], [-0.4298275], [-0.85226638], ... (i 995 pozostałych)\n", "\n", "\n" ] @@ -600,7 +588,7 @@ { "data": { "text/plain": "
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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -640,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 45, "outputs": [ { "name": "stdout", @@ -652,8 +640,8 @@ } ], "source": [ - "print(f'Średnia waga próbki wag przed dietą: {np.mean(weights_before)[0]}')\n", - "print(f'Średnia waga próbki wag po diecie: {np.mean(weights_after)[0]}')" + "print(f'Średnia waga próbki wag przed dietą: {weights_before.mean()[0]}')\n", + "print(f'Średnia waga próbki wag po diecie: {weights_after.mean()[0]}')" ], "metadata": { "collapsed": false, @@ -670,6 +658,7 @@ "source": [ "### Hipoteza\n", "H0 - Średnia waga nie uległa zmianie po zastosowaniu diety\n", + "\n", "H1 - Średnia waga po diecie jest znacząco mniejsza od wagi przed dietą\n" ] }, @@ -687,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 46, "metadata": { "collapsed": false, "pycharm": { @@ -732,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 47, "metadata": { "collapsed": false, "pycharm": { @@ -752,7 +741,7 @@ "p: 0.0\n", "Wartość statystyki testowej z próby: [48.30834167]\n", "Wartości statystyk z prób boostrapowych:\n", - "[0.520412], [-1.17045922], [0.83736887], [0.65925044], [2.48031265], ... (i 995 pozostałych)\n", + "[0.60036921], [0.39462787], [1.07826355], [1.46977585], [-0.09697788], ... (i 995 pozostałych)\n", "\n", "\n" ] @@ -760,7 +749,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" 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