From 5b9ff302459fa54fbed26d7475bbedbd3ce418e4 Mon Sep 17 00:00:00 2001 From: emkarcinos Date: Wed, 18 May 2022 12:03:27 +0200 Subject: [PATCH] Added single bootstrap test --- bootstrap-t.ipynb | 192 ++++++++++++++++++++++++++-------------------- 1 file changed, 110 insertions(+), 82 deletions(-) diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index d8baa0d..deb8a7e 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 40, "metadata": { "pycharm": { "name": "#%%\n" @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 44, "metadata": { "pycharm": { "name": "#%%\n" @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 49, "metadata": { "pycharm": { "name": "#%%\n" @@ -237,7 +237,7 @@ }, "outputs": [], "source": [ - "def generate_bootstraps(data, n_bootstraps=100):\n", + "def generate_bootstraps(data, n_bootstraps=1000):\n", " data_size = data.shape[0]\n", " for _ in range(n_bootstraps):\n", " indices = np.random.choice(len(data), size=data_size)\n", @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 50, "metadata": { "collapsed": false, "pycharm": { @@ -269,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 51, "metadata": { "collapsed": false, "pycharm": { @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 52, "metadata": { "collapsed": false, "pycharm": { @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 53, "metadata": { "collapsed": false, "pycharm": { @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 54, "metadata": { "collapsed": false, "pycharm": { @@ -355,22 +355,15 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "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" + "0 169.5557\ndtype: float64\n0 175.1417\ndtype: float64\n0 79.6342\ndtype: float64\n0 76.5602\ndtype: float64\n" ] } ], "source": [ - "dataset = pd.read_csv('experiment_data2.csv')\n", + "dataset = pd.read_csv('experiment_data.csv')\n", "heights_female = pd.DataFrame(dataset['Female height'].to_numpy()) # xd\n", "heights_male = pd.DataFrame(dataset['Male height'].to_numpy())\n", "weights_before = pd.DataFrame(dataset['Weight before'].to_numpy())\n", @@ -381,6 +374,13 @@ "print(np.mean(weights_after))\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": { @@ -391,16 +391,42 @@ "\n", "**Test t Studenta dla jednej próby** wykorzystujemy gdy chcemy porównać średnią “teoretyczną” ze średnią, którą faktycznie możemy zaobserwować w naszej bazie danych. Średnia teoretyczna to średnia pochodząca z innych badań lub po prostu bez większych uzasadnień pochodząca z naszej głowy.\n", "\n", - "Wyobraźmy sobie, że mamy dane z takimi zmiennymi jak wzrost pewnej grupy ludzi. Dzięki testowi t Studenta dla jednej próby możemy dowiedzieć się np. czy wzrost naszego młodszego brata wynoszący 155cm odbiega znacząco od średniej wzrostu tej grupy.\n", + "Wyobraźmy sobie, że mamy dane z takimi zmiennymi jak wzrost pewnej grupy ludzi. Dzięki testowi t Studenta dla jednej próby możemy dowiedzieć się np. czy wzrost naszego młodszego brata wynoszący 160cm odbiega znacząco od średniej wzrostu tej grupy.\n", "\n", "### Hipoteza\n", "\n", - "*H0: Badana próba została wylosowana z populacji, w której wzrost osób wynosi średnio 155cm.* \n", - "*H1: Badana próba nie została wylosowana z populacji gdzie średni wzrost wynosi 155cm.*\n", + "*H0: Badana próba została wylosowana z populacji, w której wzrost osób wynosi średnio 160cm.* \n", + "*H1: Badana próba została wylosowana z populacji gdzie średni wzrost jest większy 160cm.*\n", "\n", "### Sprawdzenie założeń\n" ] }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "p = 0.791\n" + ] + } + ], + "source": [ + "# Sprawdzamy, czy próby mają rozkład normalny\n", + "shapiro_test = shapiro(heights_female)\n", + "print(f\"p = {round(shapiro_test.pvalue,4)}\")" + ] + }, + { + "source": [ + "P wartość jest większa niż alfa = 0.05, więc próba ma prawdopodobnie rozkład normalny. Możemy stostować testy." + ], + "cell_type": "markdown", + "metadata": {} + }, { "cell_type": "markdown", "metadata": { @@ -412,15 +438,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Wyniki bootstrapowej wersji testu T-studenta\n\nHipoteza: średnia jest równa 160.0\nHipoteza alternatywna: średnia jest większa\n\np: 0.5\nWartość statystyki testowej z próby: [19.1207964]\nWartości statystyk z prób boostrapowych:\n[17.41702865], [19.17874674], [20.59090525], [17.666445], [19.3593138], ... (i 95 pozostałych)\n\n\n\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "tested_mean = 160.0\n", + "\n", + "p, t, ts = bootstrap_one_sample(heights_female, tested_mean, alternative=Alternatives.GREATER)\n", + "draw_distribution([x[0] for x in ts], t)" + ] }, { "cell_type": "markdown", @@ -428,7 +478,9 @@ "collapsed": false }, "source": [ - "## Wniosek" + "## Wniosek\n", + "\n", + "Nie mamy podstaw, żeby odrzucić hipotezę zerową mówiącą, że średnia wynosi 160." ] }, { @@ -455,15 +507,14 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 57, "metadata": {}, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "p = 0.791\n", - "p = 0.7535\n" + "p = 0.791\np = 0.7535\n" ] } ], @@ -495,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 58, "metadata": { "collapsed": false, "pycharm": { @@ -504,33 +555,22 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Wyniki bootstrapowej wersji testu T-studenta\n", - "\n", - "Hipoteza: średnie są takie same\n", - "Hipoteza alternatywna: średnia jest mniejsza\n", - "\n", - "p: 0.0\n", - "Wartość statystyki testowej z próby: [8.04931557]\n", - "Wartości statystyk z prób boostrapowych:\n", - "[0.36930777], [0.23100612], [-0.6106529], [-0.47586438], [0.86529699], ... (i 95 pozostałych)\n", - "\n", - "\n" + "Wyniki bootstrapowej wersji testu T-studenta\n\nHipoteza: średnie są takie same\nHipoteza alternatywna: średnia jest mniejsza\n\np: 0.0\nWartość statystyki testowej z próby: [8.04931557]\nWartości statystyk z prób boostrapowych:\n[0.2748409], [-0.61193473], [1.24335163], [-2.56879464], [0.34249038], ... (i 95 pozostałych)\n\n\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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AyydUqyTNSxMJiKq6ATigsf60vvkCThpnXZKk+23Jr7lKkibIgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVLTJMek3qI49rAk/SavICRJTWMPiCR7JflakquTXJXkdY02i5PcmeTSbnrruOuUpPluEreY1gJvqKqV3bjUlyRZXlVXz2j3zao6ZgL1SZKYwBVEVd1SVSu7+buBa4A9xl2HJGn9JvoMIski4AnAdxubn5LksiRfTPLY9RxjSZIVSVasWbNmVKVK0rwzsYBIsiPwGeDkqrprxuaVwKOq6gDg/cDnZjtOVS2rqumqmp6amhpZvZI030wkIJJsRy8czq6qz87cXlV3VdU93fwFwHZJdh1zmZI0r03iLaYAZwDXVNW7Z2nziK4dSQ6mV+dPx1elJGkSbzEdArwUuCLJpd26NwMLAarqNOCFwKuTrAV+ARxXVTWBWiVp3hp7QFTVt4BsoM2pwKnjqWjzbc63sDUe/h1JG89vUkuSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1DSpMamPSHJtkuuTLG1sf2CST3bbv5tk0QTKlKR5bRJjUi8A/gY4EtgPOD7JfjOavQK4o6r+PfAe4J3jrVKSNIkriIOB66vqhqq6F/gEcOyMNscCH+7mPw0cmmS9w5RKkoZr7GNSA3sAN/UtrwKeNFubqlqb5E7g4cBPZh4syRJgSbd4T5Jrh17x5tmVRt1bET/fLDJ3rnv9O5zbep9v0/8N/ajZNkwiIIaqqpYByyZdx2ySrKiq6UnXMSp+vrlva/+Mfr5NN4lbTKuBvfqW9+zWNdsk2RZ4GPDTsVQnSQImExAXA/sk2TvJA4DjgPNmtDkPOKGbfyHwD1VVY6xRkua9sd9i6p4pvAb4MrAAOLOqrkryDmBFVZ0HnAF8NMn1wO30QmSu2mJvfw2Jn2/u29o/o59vE8V/mEuSWvwmtSSpyYCQJDUZEGOQ5K+S/CDJ5UnOTbLTpGsahg11mTKXJdkrydeSXJ3kqiSvm3RNo5BkQZLvJ/nCpGsZtiQ7Jfl09//eNUmeMumahinJ67v/Nq9Mck6SBw37HAbEeCwH9q+qxwP/DLxpwvVstgG7TJnL1gJvqKr9gCcDJ21ln2+d1wHXTLqIEflr4EtV9RjgALaiz5lkD+C1wHRV7U/vhZ+hv8xjQIxBVV1YVWu7xe/Q++7HXDdIlylzVlXdUlUru/m76f1y2WOyVQ1Xkj2Bo4HTJ13LsCV5GPB0em9EUlX3VtW/TrSo4dsWeHD3XbHtgZuHfQIDYvz+CPjipIsYglaXKVvVL9B1ut6EnwB8d8KlDNt7gT8FfjXhOkZhb2AN8KHuFtrpSXaYdFHDUlWrgf8N/Ai4Bbizqi4c9nkMiCFJ8pXuXuDM6di+Nm+hd+vi7MlVqo2RZEfgM8DJVXXXpOsZliTHALdV1SWTrmVEtgUOAj5QVU8AfgZsNc/JkuxM74p9b+CRwA5JXjLs88z5vpi2FFV12Pq2J3kZcAxw6FbyrfBBukyZ05JsRy8czq6qz066niE7BHhekqOABwEPTfKxqhr6L5kJWQWsqqp1V32fZisKCOAw4F+qag1Aks8Cvw98bJgn8QpiDJIcQe9S/nlV9fNJ1zMkg3SZMmd13cufAVxTVe+edD3DVlVvqqo9q2oRvb+7f9iKwoGq+jFwU5J9u1WHAldPsKRh+xHw5CTbd/+tHsoIHsJ7BTEepwIPBJZ3w1p8p6pOnGxJm2e2LlMmXNYwHQK8FLgiyaXdujdX1QWTK0kb6b8BZ3f/gLkBePmE6xmaqvpukk8DK+ndtv4+I+hyw642JElN3mKSJDUZEJKkJgNCktRkQEiSmgwISVKTAaE5JcnDk1zaTT9Osrpv+QED7L84ye9vxPkWJflPG9suyXSS9w2r/eZKclGSkQxsr62XAaE5pap+WlUHVtWBwGnAe9Ytd50Gbshiet84HdQiYIMBMbNdVa2oqtcOsb00dgaE5rwkv5fk60kuSfLlJLt361/bjedweZJPdJ3unQi8vrvieNqM4zyj72rk+0keApwCPK1b9/ruX/7fTLKym9aFzcx2i9eNsTDgcfvb75jkQ0mu6Gr/wxl1HpHkU33L/ft+IMmKbpyAP5/lz+uevvkXJjmrm59K8pkkF3fTIZv6d6KtRFU5Oc3JCXg78D+AfwSmunUvpvetbuh1f/zAbn6nvn3+ZJbjfR44pJvfkV5PA4uBL/S12R54UDe/D7Cim5/Z7tfLAx63v/07gff2bdt5Rp3b0utqYYdu+QPAS7r5XbqfC4CLgMd3yxfRGzsA4J6+Y70QOKub/zjw1G5+Ib1uRib+9+w0ucmuNjTXPRDYn/u7MVlAr/tjgMvpdbXwOeBzAxzr28C7k5wNfLaqVnXH7LcdcGqSA4H7gP8wpOP2O4y+wV+q6o7+jdXr5uRLwHO77haOptfXF8CLkiyhFyK70xvM6fIBalx33v36antokh2r6p717KOtmAGhuS7AVVXVGk7yaHqDxjwXeEuSx63vQFV1SpLzgaOAbyd5TqPZ64Fb6Y1Qtg3w/zZU4IDH3VifAF4D3E7vKubuJHsDfwI8saru6G4dtYah7O9fp3/7NsCTq2qDn0nzg88gNNf9EphKN95wku2SPDbJNsBeVfU14I3Aw+jd3rkbeEjrQEkeXVVXVNU76fVW+5hG+4cBt1TVr+h15regW7+5x+23HDipb/+dG22+Tm+8g1fSCwuAh9Ib9+DOJLvRGw625dYkv9v9Gb2gb/2F9Dq4W3feA2fZX/OEAaG57lf07qO/M8llwKX03lJaAHwsyRX0erp8X/WGnPw88ILWQ2rg5PQGeboc+Dd6I/9dDtyX5LIkrwf+FjihO9dj6P1CptFuY4/b7y+Bnbt9LgOeOfNDV9V9wBfohcAXunWXdZ/1B/SeJ3x7lj+zpd0+/8j9t+OgG+O4ezB+Nb0H+prH7M1VktTkFYQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWr6/15Iu2fHJRYeAAAAAElFTkSuQmCC\n" }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], "source": [ @@ -586,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 59, "metadata": { "collapsed": false, "pycharm": { @@ -595,11 +635,10 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "p = 0.3308\n", - "p = 0.4569\n" + "p = 0.3308\np = 0.4569\n" ] } ], @@ -631,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 39, "metadata": { "collapsed": false, "pycharm": { @@ -640,33 +679,22 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Wyniki bootstrapowej wersji testu T-studenta\n", - "\n", - "Hipoteza: średnie są takie same\n", - "Hipoteza alternatywna: średnia jest mniejsza\n", - "\n", - "p: 0.0\n", - "Wartość statystyki testowej z próby: [48.30834167]\n", - "Wartości statystyk z prób boostrapowych:\n", - "[0.35583403], [0.29159863], [-1.32145739], [0.13260175], [0.79403136], ... (i 95 pozostałych)\n", - "\n", - "\n" + "Wyniki bootstrapowej wersji testu T-studenta\n\nHipoteza: średnie są takie same\nHipoteza alternatywna: średnia jest mniejsza\n\np: 0.0\nWartość statystyki testowej z próby: [48.30834167]\nWartości statystyk z prób boostrapowych:\n[-0.18332849], [-1.21537352], [1.64628473], [1.06552535], [-0.71420173], ... (i 95 pozostałych)\n\n\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n" }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], "source": [ @@ -715,7 +743,7 @@ "hash": "1b132c2ed43285dcf39f6d01712959169a14a721cf314fe69015adab49bb1fd1" }, "kernelspec": { - "display_name": "Python 3.8.10 64-bit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -729,9 +757,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.10-final" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file