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Initial implementation

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This commit is contained in:
Marcin Kostrzewski 2022-05-11 15:02:15 +02:00
parent 894644b4b8
commit 7e0cf14302
2 changed files with 658 additions and 0 deletions
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157
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{
"cells": [
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from math import sqrt\n",
"from scipy.stats import sem\n",
"from scipy.stats import t"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def generate_bootstraps(data, n_bootstraps=100):\n",
" data_size = data.shape[0]\n",
" for b in range(n_bootstraps):\n",
" indicies = np.random.choice(len(data), size=data_size)\n",
" yield data.iloc[indicies, :]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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": 49,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"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",
"\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"
]
},
{
"cell_type": "code",
"execution_count": 50,
"outputs": [],
"source": [
"def make_decision(data, columns, alpha=0.05):\n",
" t_stat, df, cv, p = independent_t_test(data, columns, alpha)\n",
" print(f't: {t_stat}, df: {df}, cv: {cv}, p: {p}\\n')\n",
" if abs(t_stat) <= cv:\n",
"\t print('Accept null hypothesis that the means are equal.')\n",
" else:\n",
" print('Reject the null hypothesis that the means are equal.')\n",
" if p > alpha:\n",
" print('Accept null hypothesis that the means are equal.')\n",
" else:\n",
"\t print('Reject the null hypothesis that the means are equal.')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 51,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t: 6.903407918031469, df: 998, cv: 1.6463818766348755, p: 9.018563673635072e-12\n",
"\n",
"Reject the null hypothesis that the means are equal.\n",
"Reject the null hypothesis that the means are equal.\n"
]
}
],
"source": [
"dataset = pd.read_csv('experiment_data.csv')\n",
"make_decision(dataset, ['Weight', 'Age'])"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"interpreter": {
"hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
},
"kernelspec": {
"display_name": "Python 3.9.1 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

501
experiment_data.csv Normal file
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Gender,Height,Weight,Index,Age
Male,174,96,4,42
Male,189,87,2,50
Female,185,110,4,43
Female,195,104,3,48
Male,149,61,3,46
Male,189,104,3,18
Male,147,92,5,57
Male,154,111,5,22
Male,174,90,3,31
Female,169,103,4,21
Male,195,81,2,72
Female,159,80,4,45
Female,192,101,3,51
Male,155,51,2,46
Male,191,79,2,40
Female,153,107,5,22
Female,157,110,5,69
Male,140,129,5,76
Male,144,145,5,21
Male,172,139,5,60
Male,157,110,5,41
Female,153,149,5,73
Female,169,97,4,21
Male,185,139,5,50
Female,172,67,2,49
Female,151,64,3,31
Male,190,95,3,67
Male,187,62,1,27
Female,163,159,5,61
Male,179,152,5,49
Male,153,121,5,22
Male,178,52,1,46
Female,195,65,1,73
Female,160,131,5,22
Female,157,153,5,42
Female,189,132,4,69
Female,197,114,3,71
Male,144,80,4,32
Female,171,152,5,29
Female,185,81,2,36
Female,175,120,4,54
Female,149,108,5,39
Male,157,56,2,23
Male,161,118,5,62
Female,182,126,4,30
Male,185,76,2,31
Female,188,122,4,30
Male,181,111,4,77
Male,161,72,3,39
Male,140,152,5,35
Female,168,135,5,80
Female,176,54,1,47
Male,163,110,5,38
Male,172,105,4,21
Male,196,116,4,39
Female,187,89,3,58
Male,172,92,4,48
Male,178,127,5,23
Female,164,70,3,76
Male,143,88,5,20
Female,191,54,0,80
Female,141,143,5,55
Male,193,54,0,54
Male,190,83,2,80
Male,175,135,5,46
Female,179,158,5,67
Female,172,96,4,53
Female,168,59,2,57
Female,164,82,4,48
Female,194,136,4,32
Female,153,51,2,32
Male,178,117,4,56
Male,141,80,5,43
Male,180,75,2,37
Female,185,100,3,18
Female,197,154,4,72
Male,165,104,4,61
Female,168,90,4,64
Female,176,122,4,69
Male,181,51,0,21
Male,164,75,3,68
Female,166,140,5,39
Female,190,105,3,47
Male,186,118,4,74
Male,168,123,5,62
Male,198,50,0,25
Female,175,141,5,58
Male,145,117,5,49
Female,159,104,5,72
Female,185,140,5,38
Female,178,154,5,66
Female,183,96,3,79
Female,194,111,3,63
Male,177,61,2,37
Male,197,119,4,40
Female,170,156,5,42
Male,142,69,4,54
Male,160,139,5,72
Male,195,69,1,35
Female,190,50,0,46
Male,199,156,4,78
Male,154,105,5,56
Male,161,155,5,79
Female,198,145,4,70
Female,192,140,4,30
Male,195,126,4,42
Male,166,160,5,42
Male,159,154,5,78
Female,181,106,4,28
Male,149,66,3,18
Female,150,70,4,19
Female,146,157,5,22
Male,190,135,4,76
Female,192,90,2,38
Female,177,96,4,72
Male,148,60,3,51
Female,165,57,2,60
Female,146,104,5,57
Male,144,108,5,54
Female,176,156,5,35
Female,168,87,4,35
Male,187,122,4,71
Male,187,138,4,57
Female,184,160,5,56
Female,158,149,5,32
Male,158,96,4,39
Male,194,115,4,56
Female,145,79,4,74
Male,182,151,5,67
Male,154,54,2,68
Female,168,139,5,32
Female,187,70,2,40
Female,158,153,5,68
Female,167,110,4,33
Female,171,155,5,60
Female,183,150,5,40
Female,190,156,5,28
Male,194,108,3,38
Male,171,147,5,58
Male,159,124,5,34
Female,169,54,2,51
Female,167,85,4,69
Male,180,149,5,60
Male,163,123,5,22
Male,140,79,5,74
Male,197,125,4,66
Male,194,106,3,66
Female,140,146,5,18
Male,195,98,3,62
Female,168,115,3,19
Female,196,50,0,20
Male,140,52,3,49
Female,150,60,3,36
Female,168,140,5,57
Female,155,111,5,62
Female,179,103,4,52
Female,182,84,3,73
Male,168,160,5,62
Female,187,102,3,48
Male,181,105,4,72
Male,199,99,2,61
Female,184,76,2,29
Male,192,101,3,57
Female,182,143,5,33
Female,172,111,4,55
Male,181,78,2,72
Male,176,109,4,47
Female,156,106,5,69
Female,151,67,3,24
Female,188,80,2,29
Male,187,136,4,18
Male,174,138,5,45
Male,167,151,5,74
Female,196,131,4,60
Male,197,149,4,27
Female,185,119,4,31
Female,170,102,4,54
Female,181,94,3,59
Female,166,126,5,72
Male,188,100,3,20
Female,162,74,3,25
Male,177,117,4,65
Male,162,97,4,67
Male,180,73,2,34
Female,192,108,3,58
Male,165,80,3,23
Female,167,135,5,19
Female,182,84,3,57
Female,161,134,5,50
Male,158,95,4,35
Male,141,85,5,49
Male,154,100,5,80
Male,165,105,4,31
Female,142,137,5,23
Male,141,94,5,35
Male,145,108,5,42
Male,157,74,4,55
Female,177,117,4,24
Female,166,144,5,73
Male,193,151,5,72
Male,184,57,1,65
Male,179,93,3,31
Female,156,89,4,25
Male,182,104,4,30
Male,145,160,5,60
Female,150,87,4,63
Male,145,99,5,55
Female,196,122,4,46
Male,191,96,3,60
Female,148,67,4,65
Female,150,84,4,45
Male,148,155,5,36
Female,153,146,5,49
Female,196,159,5,20
Female,185,52,0,74
Female,171,131,5,50
Female,143,118,5,41
Female,142,86,5,77
Female,141,126,5,37
Male,159,109,5,74
Female,173,82,2,18
Male,183,138,5,65
Female,152,90,4,24
Male,178,140,5,56
Male,188,54,0,20
Female,155,144,5,58
Male,166,70,3,68
Male,188,123,4,31
Female,171,120,5,51
Male,179,130,5,41
Female,186,137,4,77
Female,153,78,2,51
Female,184,86,3,56
Female,177,81,3,31
Male,145,78,4,39
Male,170,81,3,37
Male,181,141,5,56
Male,165,155,5,18
Female,174,65,2,57
Female,146,110,5,65
Male,178,85,3,38
Male,166,61,2,35
Male,191,62,1,38
Female,177,155,5,62
Female,183,50,0,27
Male,151,114,5,55
Male,182,98,3,30
Female,142,159,5,61
Female,188,90,3,19
Male,161,89,4,35
Male,153,70,3,70
Male,140,143,5,78
Male,169,141,5,48
Female,162,159,5,41
Male,183,147,5,37
Female,162,58,2,77
Female,172,109,4,28
Female,150,119,5,49
Female,169,145,5,74
Female,184,132,4,23
Male,159,104,5,44
Male,163,131,5,22
Male,156,137,5,45
Female,157,52,2,79
Male,147,84,4,77
Male,141,86,5,32
Male,173,139,5,59
Male,154,145,5,75
Male,168,148,5,21
Male,168,50,1,71
Male,145,130,5,24
Male,152,103,5,36
Female,187,121,4,25
Female,163,57,0,30
Male,178,83,3,24
Female,187,94,3,35
Female,179,114,4,25
Male,190,80,2,45
Male,172,75,3,49
Male,188,57,1,25
Male,193,65,1,66
Female,147,126,5,40
Female,147,94,5,72
Male,166,107,4,27
Female,192,139,4,63
Male,181,139,4,41
Male,150,74,4,50
Male,178,160,5,48
Female,156,52,2,80
Male,149,100,5,22
Male,156,74,4,64
Male,183,105,3,30
Female,162,68,3,43
Female,165,83,4,27
Female,168,143,5,53
Male,160,156,5,36
Female,169,88,2,75
Female,140,76,4,23
Female,187,92,3,28
Male,151,82,4,34
Female,186,140,5,79
Male,182,108,4,48
Male,188,81,2,25
Male,179,110,4,45
Female,156,126,5,59
Male,188,114,4,19
Male,183,153,5,72
Male,144,88,5,53
Male,196,69,1,40
Male,171,141,5,26
Male,171,147,5,51
Female,180,156,5,68
Male,191,146,5,65
Female,179,67,2,77
Female,180,60,2,71
Female,154,132,5,19
Male,188,99,3,54
Male,142,135,5,74
Male,170,95,4,55
Male,152,141,5,66
Female,190,118,4,37
Female,181,111,4,73
Male,153,104,5,26
Male,187,140,5,61
Female,144,66,4,20
Female,148,54,2,48
Female,199,92,2,49
Female,167,85,4,73
Female,164,71,3,19
Female,185,102,3,65
Female,164,160,5,77
Male,142,71,4,50
Male,165,68,2,59
Female,172,62,2,42
Female,157,56,2,18
Male,155,57,2,37
Female,167,153,5,79
Female,164,126,5,80
Female,189,125,4,39
Female,161,145,5,32
Female,155,71,3,56
Female,171,118,4,74
Female,154,92,4,34
Male,179,83,3,53
Male,170,115,4,73
Female,184,106,4,54
Female,191,68,2,60
Male,162,58,2,22
Male,178,138,5,61
Female,157,60,2,35
Male,184,83,2,59
Male,197,88,2,68
Female,160,51,2,34
Male,184,153,5,65
Male,190,50,0,18
Male,174,90,3,49
Female,189,124,4,80
Female,186,143,5,55
Female,180,58,1,41
Female,186,148,4,42
Female,193,61,1,21
Male,161,103,4,33
Female,151,158,5,50
Female,195,147,4,42
Female,184,152,5,80
Male,141,80,5,69
Female,185,94,3,47
Female,186,127,4,37
Male,142,131,5,68
Female,147,67,4,36
Male,151,62,3,72
Female,160,124,5,67
Male,185,60,1,79
Female,163,63,2,42
Male,174,95,4,29
Female,150,144,5,46
Male,142,91,5,61
Male,178,142,5,20
Female,154,96,5,69
Male,176,87,3,75
Male,159,120,5,22
Male,191,62,1,32
Male,177,117,4,66
Male,151,154,5,62
Female,182,149,5,56
Female,197,72,2,57
Male,146,138,5,25
Female,160,83,4,65
Female,157,66,3,44
Female,150,50,2,60
Female,167,58,2,51
Female,180,70,2,29
Female,183,76,2,79
Female,183,87,3,58
Female,152,154,5,21
Female,164,71,3,74
Male,187,96,3,69
Male,169,136,5,44
Female,149,61,3,75
Male,163,137,5,32
Female,195,104,3,76
Male,174,107,4,65
Male,182,70,2,60
Male,169,110,4,53
Male,193,130,4,63
Male,148,141,5,59
Male,186,68,2,48
Male,165,143,5,59
Female,146,123,5,53
Female,166,133,5,26
Male,179,56,1,45
Female,177,101,4,36
Male,181,154,5,66
Female,161,154,5,65
Female,157,103,5,25
Female,169,98,4,80
Female,152,114,5,30
Female,162,64,2,67
Male,162,130,5,57
Female,177,61,2,48
Female,195,61,1,56
Male,140,146,5,69
Female,186,146,5,79
Female,178,107,4,51
Male,174,54,1,18
Female,180,59,1,26
Male,188,141,4,51
Female,187,130,4,18
Female,153,77,4,75
Female,165,95,4,51
Female,178,79,2,79
Female,163,154,5,28
Female,150,97,5,77
Male,179,127,4,20
Male,165,62,2,58
Male,168,158,5,67
Female,153,133,5,28
Male,184,157,5,24
Male,188,65,1,64
Female,166,153,5,33
Female,172,116,4,22
Male,182,73,2,74
Male,143,149,5,54
Male,152,146,5,36
Female,186,128,4,26
Male,159,140,5,70
Male,146,70,4,54
Female,176,121,4,35
Female,146,101,5,49
Male,159,145,5,31
Male,162,157,5,30
Female,172,90,4,78
Female,169,121,5,54
Male,182,50,0,36
Female,183,79,2,41
Male,176,77,2,60
Female,188,128,4,53
Female,175,83,2,41
Male,154,81,4,28
Female,184,147,5,43
Male,179,123,4,21
Male,152,132,5,49
Male,179,56,1,49
Female,145,141,5,77
Female,181,80,2,20
Male,158,127,5,30
Female,188,99,3,76
Male,145,142,5,61
Male,161,115,5,39
Male,198,109,3,69
Male,147,142,5,47
Male,154,112,5,21
Female,178,65,2,70
Male,195,153,5,43
Female,167,79,3,54
Male,183,131,4,73
Female,164,142,5,46
Male,167,64,2,33
Female,151,55,2,36
Female,147,107,5,68
Female,155,115,5,47
Female,172,108,4,68
Female,142,86,5,41
Male,146,85,4,38
Female,188,115,4,25
Male,173,111,4,21
Female,160,109,5,30
Male,187,80,2,75
Male,198,136,4,78
Female,179,150,5,36
Female,164,59,2,62
Female,146,147,5,37
Female,198,50,0,56
Female,170,53,1,59
Male,152,98,5,53
Female,150,153,5,59
Female,184,121,4,37
Female,141,136,5,52
Male,150,95,5,28
Male,173,131,5,66
1 Gender Height Weight Index Age
2 Male 174 96 4 42
3 Male 189 87 2 50
4 Female 185 110 4 43
5 Female 195 104 3 48
6 Male 149 61 3 46
7 Male 189 104 3 18
8 Male 147 92 5 57
9 Male 154 111 5 22
10 Male 174 90 3 31
11 Female 169 103 4 21
12 Male 195 81 2 72
13 Female 159 80 4 45
14 Female 192 101 3 51
15 Male 155 51 2 46
16 Male 191 79 2 40
17 Female 153 107 5 22
18 Female 157 110 5 69
19 Male 140 129 5 76
20 Male 144 145 5 21
21 Male 172 139 5 60
22 Male 157 110 5 41
23 Female 153 149 5 73
24 Female 169 97 4 21
25 Male 185 139 5 50
26 Female 172 67 2 49
27 Female 151 64 3 31
28 Male 190 95 3 67
29 Male 187 62 1 27
30 Female 163 159 5 61
31 Male 179 152 5 49
32 Male 153 121 5 22
33 Male 178 52 1 46
34 Female 195 65 1 73
35 Female 160 131 5 22
36 Female 157 153 5 42
37 Female 189 132 4 69
38 Female 197 114 3 71
39 Male 144 80 4 32
40 Female 171 152 5 29
41 Female 185 81 2 36
42 Female 175 120 4 54
43 Female 149 108 5 39
44 Male 157 56 2 23
45 Male 161 118 5 62
46 Female 182 126 4 30
47 Male 185 76 2 31
48 Female 188 122 4 30
49 Male 181 111 4 77
50 Male 161 72 3 39
51 Male 140 152 5 35
52 Female 168 135 5 80
53 Female 176 54 1 47
54 Male 163 110 5 38
55 Male 172 105 4 21
56 Male 196 116 4 39
57 Female 187 89 3 58
58 Male 172 92 4 48
59 Male 178 127 5 23
60 Female 164 70 3 76
61 Male 143 88 5 20
62 Female 191 54 0 80
63 Female 141 143 5 55
64 Male 193 54 0 54
65 Male 190 83 2 80
66 Male 175 135 5 46
67 Female 179 158 5 67
68 Female 172 96 4 53
69 Female 168 59 2 57
70 Female 164 82 4 48
71 Female 194 136 4 32
72 Female 153 51 2 32
73 Male 178 117 4 56
74 Male 141 80 5 43
75 Male 180 75 2 37
76 Female 185 100 3 18
77 Female 197 154 4 72
78 Male 165 104 4 61
79 Female 168 90 4 64
80 Female 176 122 4 69
81 Male 181 51 0 21
82 Male 164 75 3 68
83 Female 166 140 5 39
84 Female 190 105 3 47
85 Male 186 118 4 74
86 Male 168 123 5 62
87 Male 198 50 0 25
88 Female 175 141 5 58
89 Male 145 117 5 49
90 Female 159 104 5 72
91 Female 185 140 5 38
92 Female 178 154 5 66
93 Female 183 96 3 79
94 Female 194 111 3 63
95 Male 177 61 2 37
96 Male 197 119 4 40
97 Female 170 156 5 42
98 Male 142 69 4 54
99 Male 160 139 5 72
100 Male 195 69 1 35
101 Female 190 50 0 46
102 Male 199 156 4 78
103 Male 154 105 5 56
104 Male 161 155 5 79
105 Female 198 145 4 70
106 Female 192 140 4 30
107 Male 195 126 4 42
108 Male 166 160 5 42
109 Male 159 154 5 78
110 Female 181 106 4 28
111 Male 149 66 3 18
112 Female 150 70 4 19
113 Female 146 157 5 22
114 Male 190 135 4 76
115 Female 192 90 2 38
116 Female 177 96 4 72
117 Male 148 60 3 51
118 Female 165 57 2 60
119 Female 146 104 5 57
120 Male 144 108 5 54
121 Female 176 156 5 35
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