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@hagino3000
Created April 25, 2016 08:17
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Probability density of CVR 4%
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{
"cells": [
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import scipy.stats\n",
"from __future__ import division"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 250人でテストしてコンバージョン率が4%のCVR分布\n",
"\n",
"4%と表記されているので250人中の10人がコンバージョンしたとする \n",
"ベルヌーイ分布のパラメータpの分布をベータ分布で求める"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.04"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10/250"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = np.linspace(0, 1, 500)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# beta分布\n",
"Y_CDF = scipy.stats.beta.cdf(X, 10, 240)\n",
"Y_PDF = scipy.stats.beta.pdf(X, 10, 240)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10ffc4c10>"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mgEnP1sRyEQlLCjuvqa3ybDcm6WNgdw12X6PblYiI9InCzkNsoB1qqz05QAXAHJcIySm6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\nSE9P58MPP6SxsZGPP/6Yb33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"text/plain": [
"<matplotlib.figure.Figure at 0x10ffcea50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X, Y_PDF)\n",
"plt.xlim(0, 0.2)\n",
"plt.ylabel(u'尤度')\n",
"plt.xlabel('Probability')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame({'x':X, 'pdf':Y_PDF, 'cdf':Y_CDF})"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0200400801603\n",
"0.0661322645291\n"
]
}
],
"source": [
"# 95%信頼区間\n",
"print(df.query('0.025 <= cdf <= 0.975').x.min())\n",
"print(df.query('0.025 <= cdf <= 0.975').x.max())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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