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parthi2929/citest_results_1.py

Created August 24, 2018 07:11
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This is testing if Adam's simplified version works
Raw
citest_results_1.py
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is simpler version by [Adam](https://www.linkedin.com/in/adam-sherwin-9202018/) in order to help me fix the problem [here](https://math.stackexchange.com/questions/2886986/confidence-intervals-inconsistent-statistical-results). His code is [here](https://gist.github.com/absherwin/9ea495333f3f269c6edec3ac7dd0b978)\n",
"\n",
"## Test 1: \n",
"\n",
"Testing for a single experiment size and sample size without visualization. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"population mean: 0.6 popoulation SD: 0.24002400240023808\n",
"[N, n, accuracy]\n",
"[(40, 200, 98.0)]\n"
]
}
],
"source": [
"from __future__ import division\n",
"from random import *\n",
"from math import sqrt\n",
"\n",
"def repeated_experiments_with_CI_2(population,mu, sigma, N_list=[], n_list=[], mode=1, dist=0, format='b'):\n",
" \"\"\"\n",
" population - raw population from which sample to be taken\n",
" mu, sigma - population parameters\n",
" C - 85% CI constant (for eg, 1.96 or 2.093 etc)\n",
" N_list - Experiment size or no of times experiments to be conducted per trial\n",
" n_list - Sample size or no of samples per experiment\n",
" Mode 1 - use population SD\n",
" Mode 2 - use unbiased sample SD\n",
" Mode 3 - usebiased sample SD\n",
" dist - 0 - use Z distribution\n",
" dist - 1 - use t distribution\n",
" \"\"\"\n",
" accuracy_list = []\n",
" for each_N in N_list: # no of experiments\n",
" for each_n in n_list: # sample size for each experiment\n",
" err_count = 0\n",
" for each_E in range(each_N): # for each experiment of experiment size\n",
" Y_hat = sample(population, k=each_n) # pick n samples\n",
" Y_mean = sum(Y_hat)/len(Y_hat)\n",
" \n",
" if dist == 0:\n",
" C = 1.96\n",
" elif dist == 1:\n",
" from scipy import stats\n",
" C = stats.t.ppf(1-0.025, each_n-1) # t value varies depending on degrees of freedom which is (no of samples - 1) \n",
"\n",
" if mode == 1:\n",
" Y_sigma = sigma\n",
" elif mode == 2:\n",
" Y_variance = sum([(y - Y_mean)**2 for y in Y_hat])/(each_n-1) # unbiased estimator\n",
" Y_sigma = round(sqrt(Y_variance), 4)\n",
" elif mode == 3:\n",
" Y_variance = sum([(y - Y_mean)**2 for y in Y_hat])/(each_n) # biased estimator\n",
" Y_sigma = round(sqrt(Y_variance), 4)\n",
" else:\n",
" raise ValueError('Wrong mode')\n",
"\n",
" CI_err = round(C*(Y_sigma/sqrt(each_n)),4)\n",
" low_err = Y_mean - CI_err\n",
" hig_err = Y_mean + CI_err\n",
" #print(CI_err,Y_mean,low_err,hig_err,mu)\n",
" if (hig_err <= mu) or (low_err >= mu):\n",
" err_count += 1\n",
" accuracy = round((1-err_count/100)*100,4)\n",
" success = 0 if accuracy < 95 else 1\n",
" if format == 'b':\n",
" accuracy_list.append((each_N, each_n, success))\n",
" else: # anything other than b\n",
" accuracy_list.append((each_N, each_n, accuracy))\n",
" return accuracy_list\n",
"\n",
"\n",
"def create_bernoulli_population(T, p):\n",
" y_freq = int(p*T) \n",
" y_pops = [1]*y_freq \n",
" o_freq = int((1-p)*T)\n",
" o_pops = [0]*o_freq\n",
" population = y_pops + o_pops\n",
" population_freq = [o_freq, y_freq]\n",
" shuffle(population) \n",
" return population, population_freq\n",
"\n",
"\n",
"population=create_bernoulli_population(10000,0.6)[0]\n",
"\n",
"mean=lambda x:sum(x)/len(x)\n",
"sd=lambda l:sum((x-mean(l))**2 for x in l)/(len(l)-1)\n",
"m=mean(population)\n",
"s=sd(population)\n",
"print('population mean: {} popoulation SD: {}'.format(m,s))\n",
"print('[N, n, accuracy]')\n",
"print(repeated_experiments_with_CI_2(population,m,s,[40],[200],2,0,'_'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Result: <font color='green'>Passed</font>\n",
"\n",
"## Test 2:\n",
"\n",
"Testing for fixed experiment size and sample size multiple times in a loop due to doubt on performance of random functions when repeatedly called. No visualization. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
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"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 95.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
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"[(40, 200, 99.0)]\n",
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"[(40, 200, 99.0)]\n",
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"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
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"[(40, 200, 97.0)]\n",
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"[(40, 200, 95.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
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"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 96.0)]\n",
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"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 96.0)]\n",
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"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
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"[(40, 200, 99.0)]\n",
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"[(40, 200, 100.0)]\n",
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"[(40, 200, 98.0)]\n",
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"[(40, 200, 98.0)]\n",
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"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
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"[(40, 200, 96.0)]\n",
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"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
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"[(40, 200, 97.0)]\n",
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"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
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"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
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"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
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"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
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"[(40, 200, 99.0)]\n",
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"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 93.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 95.0)]\n",
"[(40, 200, 96.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 94.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 100.0)]\n",
"[(40, 200, 98.0)]\n",
"[(40, 200, 99.0)]\n",
"[(40, 200, 97.0)]\n",
"[(40, 200, 99.0)]\n"
]
}
],
"source": [
"for i in range(5,500,5):\n",
" for j in range(5,80,5):\n",
" print(repeated_experiments_with_CI_2(population,m,s,[40],[200],2,0,'_'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Result: <font color='green'>Passed</font>\n",
"\n",
"## Test 3: \n",
"\n",
"If we check for couple of experiment sizes and sample sizes. No visualization. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"population mean: 0.6 popoulation SD: 0.24002400240023822\n",
"[N, n, accuracy]\n",
"[(5, 5, 100.0)]\n",
"[(5, 10, 99.0)]\n",
"[(5, 15, 99.0)]\n",
"[(5, 20, 100.0)]\n",
"[(5, 25, 100.0)]\n",
"[(5, 30, 99.0)]\n",
"[(5, 35, 100.0)]\n",
"[(5, 40, 100.0)]\n",
"[(5, 45, 100.0)]\n",
"[(5, 50, 100.0)]\n",
"[(5, 55, 98.0)]\n",
"[(5, 60, 100.0)]\n",
"[(5, 65, 100.0)]\n",
"[(5, 70, 99.0)]\n",
"[(5, 75, 100.0)]\n",
"[(10, 5, 96.0)]\n",
"[(10, 10, 100.0)]\n",
"[(10, 15, 98.0)]\n",
"[(10, 20, 99.0)]\n",
"[(10, 25, 98.0)]\n",
"[(10, 30, 100.0)]\n",
"[(10, 35, 100.0)]\n",
"[(10, 40, 100.0)]\n",
"[(10, 45, 100.0)]\n",
"[(10, 50, 99.0)]\n",
"[(10, 55, 100.0)]\n",
"[(10, 60, 99.0)]\n",
"[(10, 65, 100.0)]\n",
"[(10, 70, 100.0)]\n",
"[(10, 75, 99.0)]\n",
"[(15, 5, 97.0)]\n",
"[(15, 10, 99.0)]\n",
"[(15, 15, 99.0)]\n",
"[(15, 20, 99.0)]\n",
"[(15, 25, 99.0)]\n",
"[(15, 30, 98.0)]\n",
"[(15, 35, 100.0)]\n",
"[(15, 40, 99.0)]\n",
"[(15, 45, 97.0)]\n",
"[(15, 50, 99.0)]\n",
"[(15, 55, 99.0)]\n",
"[(15, 60, 100.0)]\n",
"[(15, 65, 98.0)]\n",
"[(15, 70, 99.0)]\n",
"[(15, 75, 98.0)]\n",
"[(20, 5, 95.0)]\n",
"[(20, 10, 98.0)]\n",
"[(20, 15, 97.0)]\n",
"[(20, 20, 99.0)]\n",
"[(20, 25, 97.0)]\n",
"[(20, 30, 98.0)]\n",
"[(20, 35, 99.0)]\n",
"[(20, 40, 96.0)]\n",
"[(20, 45, 99.0)]\n",
"[(20, 50, 100.0)]\n",
"[(20, 55, 99.0)]\n",
"[(20, 60, 100.0)]\n",
"[(20, 65, 100.0)]\n",
"[(20, 70, 100.0)]\n",
"[(20, 75, 99.0)]\n",
"[(25, 5, 93.0)]\n",
"[(25, 10, 96.0)]\n",
"[(25, 15, 100.0)]\n",
"[(25, 20, 98.0)]\n",
"[(25, 25, 99.0)]\n",
"[(25, 30, 96.0)]\n",
"[(25, 35, 100.0)]\n",
"[(25, 40, 98.0)]\n",
"[(25, 45, 96.0)]\n",
"[(25, 50, 98.0)]\n",
"[(25, 55, 100.0)]\n",
"[(25, 60, 98.0)]\n",
"[(25, 65, 99.0)]\n",
"[(25, 70, 99.0)]\n",
"[(25, 75, 98.0)]\n",
"[(30, 5, 97.0)]\n",
"[(30, 10, 98.0)]\n",
"[(30, 15, 100.0)]\n",
"[(30, 20, 97.0)]\n",
"[(30, 25, 99.0)]\n",
"[(30, 30, 97.0)]\n",
"[(30, 35, 98.0)]\n",
"[(30, 40, 98.0)]\n",
"[(30, 45, 99.0)]\n",
"[(30, 50, 98.0)]\n",
"[(30, 55, 97.0)]\n",
"[(30, 60, 98.0)]\n",
"[(30, 65, 98.0)]\n",
"[(30, 70, 97.0)]\n",
"[(30, 75, 95.0)]\n",
"[(35, 5, 98.0)]\n",
"[(35, 10, 94.0)]\n",
"[(35, 15, 97.0)]\n",
"[(35, 20, 96.0)]\n",
"[(35, 25, 100.0)]\n",
"[(35, 30, 99.0)]\n",
"[(35, 35, 100.0)]\n",
"[(35, 40, 99.0)]\n",
"[(35, 45, 95.0)]\n",
"[(35, 50, 99.0)]\n",
"[(35, 55, 99.0)]\n",
"[(35, 60, 98.0)]\n",
"[(35, 65, 98.0)]\n",
"[(35, 70, 100.0)]\n",
"[(35, 75, 97.0)]\n",
"[(40, 5, 90.0)]\n",
"[(40, 10, 96.0)]\n",
"[(40, 15, 98.0)]\n",
"[(40, 20, 98.0)]\n",
"[(40, 25, 96.0)]\n",
"[(40, 30, 97.0)]\n",
"[(40, 35, 97.0)]\n",
"[(40, 40, 99.0)]\n",
"[(40, 45, 97.0)]\n",
"[(40, 50, 96.0)]\n",
"[(40, 55, 95.0)]\n",
"[(40, 60, 98.0)]\n",
"[(40, 65, 97.0)]\n",
"[(40, 70, 99.0)]\n",
"[(40, 75, 99.0)]\n",
"[(45, 5, 93.0)]\n",
"[(45, 10, 96.0)]\n",
"[(45, 15, 98.0)]\n",
"[(45, 20, 99.0)]\n",
"[(45, 25, 100.0)]\n",
"[(45, 30, 100.0)]\n",
"[(45, 35, 97.0)]\n",
"[(45, 40, 100.0)]\n",
"[(45, 45, 96.0)]\n",
"[(45, 50, 98.0)]\n",
"[(45, 55, 97.0)]\n",
"[(45, 60, 95.0)]\n",
"[(45, 65, 98.0)]\n",
"[(45, 70, 97.0)]\n",
"[(45, 75, 95.0)]\n",
"[(50, 5, 89.0)]\n",
"[(50, 10, 95.0)]\n",
"[(50, 15, 99.0)]\n",
"[(50, 20, 97.0)]\n",
"[(50, 25, 95.0)]\n",
"[(50, 30, 98.0)]\n",
"[(50, 35, 95.0)]\n",
"[(50, 40, 94.0)]\n",
"[(50, 45, 95.0)]\n",
"[(50, 50, 98.0)]\n",
"[(50, 55, 98.0)]\n",
"[(50, 60, 97.0)]\n",
"[(50, 65, 98.0)]\n",
"[(50, 70, 97.0)]\n",
"[(50, 75, 99.0)]\n",
"[(55, 5, 92.0)]\n",
"[(55, 10, 91.0)]\n",
"[(55, 15, 96.0)]\n",
"[(55, 20, 97.0)]\n",
"[(55, 25, 97.0)]\n",
"[(55, 30, 99.0)]\n",
"[(55, 35, 98.0)]\n",
"[(55, 40, 97.0)]\n",
"[(55, 45, 97.0)]\n",
"[(55, 50, 96.0)]\n",
"[(55, 55, 96.0)]\n",
"[(55, 60, 96.0)]\n",
"[(55, 65, 97.0)]\n",
"[(55, 70, 100.0)]\n",
"[(55, 75, 98.0)]\n",
"[(60, 5, 92.0)]\n",
"[(60, 10, 90.0)]\n",
"[(60, 15, 98.0)]\n",
"[(60, 20, 95.0)]\n",
"[(60, 25, 97.0)]\n",
"[(60, 30, 97.0)]\n",
"[(60, 35, 97.0)]\n",
"[(60, 40, 98.0)]\n",
"[(60, 45, 97.0)]\n",
"[(60, 50, 95.0)]\n",
"[(60, 55, 95.0)]\n",
"[(60, 60, 98.0)]\n",
"[(60, 65, 96.0)]\n",
"[(60, 70, 98.0)]\n",
"[(60, 75, 97.0)]\n",
"[(65, 5, 92.0)]\n",
"[(65, 10, 95.0)]\n",
"[(65, 15, 93.0)]\n",
"[(65, 20, 90.0)]\n",
"[(65, 25, 97.0)]\n",
"[(65, 30, 97.0)]\n",
"[(65, 35, 93.0)]\n",
"[(65, 40, 97.0)]\n",
"[(65, 45, 95.0)]\n",
"[(65, 50, 98.0)]\n",
"[(65, 55, 90.0)]\n",
"[(65, 60, 94.0)]\n",
"[(65, 65, 97.0)]\n",
"[(65, 70, 96.0)]\n",
"[(65, 75, 96.0)]\n",
"[(70, 5, 82.0)]\n",
"[(70, 10, 91.0)]\n",
"[(70, 15, 98.0)]\n",
"[(70, 20, 95.0)]\n",
"[(70, 25, 98.0)]\n",
"[(70, 30, 97.0)]\n",
"[(70, 35, 96.0)]\n",
"[(70, 40, 95.0)]\n",
"[(70, 45, 99.0)]\n",
"[(70, 50, 99.0)]\n",
"[(70, 55, 95.0)]\n",
"[(70, 60, 95.0)]\n",
"[(70, 65, 99.0)]\n",
"[(70, 70, 97.0)]\n",
"[(70, 75, 91.0)]\n",
"[(75, 5, 85.0)]\n",
"[(75, 10, 95.0)]\n",
"[(75, 15, 96.0)]\n",
"[(75, 20, 95.0)]\n",
"[(75, 25, 96.0)]\n",
"[(75, 30, 95.0)]\n",
"[(75, 35, 98.0)]\n",
"[(75, 40, 99.0)]\n",
"[(75, 45, 95.0)]\n",
"[(75, 50, 99.0)]\n",
"[(75, 55, 96.0)]\n",
"[(75, 60, 97.0)]\n",
"[(75, 65, 96.0)]\n",
"[(75, 70, 95.0)]\n",
"[(75, 75, 95.0)]\n",
"[(80, 5, 83.0)]\n",
"[(80, 10, 92.0)]\n",
"[(80, 15, 94.0)]\n",
"[(80, 20, 88.0)]\n",
"[(80, 25, 95.0)]\n",
"[(80, 30, 91.0)]\n",
"[(80, 35, 98.0)]\n",
"[(80, 40, 96.0)]\n",
"[(80, 45, 94.0)]\n",
"[(80, 50, 96.0)]\n",
"[(80, 55, 93.0)]\n",
"[(80, 60, 94.0)]\n",
"[(80, 65, 99.0)]\n",
"[(80, 70, 92.0)]\n",
"[(80, 75, 94.0)]\n",
"[(85, 5, 88.0)]\n",
"[(85, 10, 89.0)]\n",
"[(85, 15, 96.0)]\n",
"[(85, 20, 92.0)]\n",
"[(85, 25, 93.0)]\n",
"[(85, 30, 92.0)]\n",
"[(85, 35, 93.0)]\n",
"[(85, 40, 95.0)]\n",
"[(85, 45, 98.0)]\n",
"[(85, 50, 97.0)]\n",
"[(85, 55, 92.0)]\n",
"[(85, 60, 97.0)]\n",
"[(85, 65, 95.0)]\n",
"[(85, 70, 96.0)]\n",
"[(85, 75, 95.0)]\n",
"[(90, 5, 92.0)]\n",
"[(90, 10, 89.0)]\n",
"[(90, 15, 94.0)]\n",
"[(90, 20, 94.0)]\n",
"[(90, 25, 98.0)]\n",
"[(90, 30, 96.0)]\n",
"[(90, 35, 94.0)]\n",
"[(90, 40, 92.0)]\n",
"[(90, 45, 94.0)]\n",
"[(90, 50, 96.0)]\n",
"[(90, 55, 88.0)]\n",
"[(90, 60, 99.0)]\n",
"[(90, 65, 95.0)]\n",
"[(90, 70, 93.0)]\n",
"[(90, 75, 95.0)]\n",
"[(95, 5, 81.0)]\n",
"[(95, 10, 87.0)]\n",
"[(95, 15, 97.0)]\n",
"[(95, 20, 97.0)]\n",
"[(95, 25, 93.0)]\n",
"[(95, 30, 91.0)]\n",
"[(95, 35, 94.0)]\n",
"[(95, 40, 94.0)]\n",
"[(95, 45, 92.0)]\n",
"[(95, 50, 96.0)]\n",
"[(95, 55, 99.0)]\n",
"[(95, 60, 98.0)]\n",
"[(95, 65, 95.0)]\n",
"[(95, 70, 95.0)]\n",
"[(95, 75, 91.0)]\n",
"[(100, 5, 81.0)]\n",
"[(100, 10, 90.0)]\n",
"[(100, 15, 97.0)]\n",
"[(100, 20, 90.0)]\n",
"[(100, 25, 88.0)]\n",
"[(100, 30, 93.0)]\n",
"[(100, 35, 90.0)]\n",
"[(100, 40, 93.0)]\n",
"[(100, 45, 95.0)]\n",
"[(100, 50, 95.0)]\n",
"[(100, 55, 98.0)]\n",
"[(100, 60, 95.0)]\n",
"[(100, 65, 93.0)]\n",
"[(100, 70, 98.0)]\n",
"[(100, 75, 93.0)]\n",
"[(105, 5, 83.0)]\n",
"[(105, 10, 96.0)]\n",
"[(105, 15, 92.0)]\n",
"[(105, 20, 96.0)]\n",
"[(105, 25, 94.0)]\n",
"[(105, 30, 95.0)]\n",
"[(105, 35, 92.0)]\n",
"[(105, 40, 94.0)]\n",
"[(105, 45, 94.0)]\n",
"[(105, 50, 99.0)]\n",
"[(105, 55, 94.0)]\n",
"[(105, 60, 93.0)]\n",
"[(105, 65, 95.0)]\n",
"[(105, 70, 96.0)]\n",
"[(105, 75, 91.0)]\n",
"[(110, 5, 88.0)]\n",
"[(110, 10, 91.0)]\n",
"[(110, 15, 90.0)]\n",
"[(110, 20, 97.0)]\n",
"[(110, 25, 95.0)]\n",
"[(110, 30, 97.0)]\n",
"[(110, 35, 95.0)]\n",
"[(110, 40, 93.0)]\n",
"[(110, 45, 94.0)]\n",
"[(110, 50, 90.0)]\n",
"[(110, 55, 98.0)]\n",
"[(110, 60, 95.0)]\n",
"[(110, 65, 93.0)]\n",
"[(110, 70, 94.0)]\n",
"[(110, 75, 94.0)]\n",
"[(115, 5, 78.0)]\n",
"[(115, 10, 88.0)]\n",
"[(115, 15, 95.0)]\n",
"[(115, 20, 97.0)]\n",
"[(115, 25, 93.0)]\n",
"[(115, 30, 95.0)]\n",
"[(115, 35, 95.0)]\n",
"[(115, 40, 95.0)]\n",
"[(115, 45, 94.0)]\n",
"[(115, 50, 95.0)]\n",
"[(115, 55, 90.0)]\n",
"[(115, 60, 97.0)]\n",
"[(115, 65, 95.0)]\n",
"[(115, 70, 94.0)]\n",
"[(115, 75, 93.0)]\n",
"[(120, 5, 82.0)]\n",
"[(120, 10, 86.0)]\n",
"[(120, 15, 95.0)]\n",
"[(120, 20, 92.0)]\n",
"[(120, 25, 88.0)]\n",
"[(120, 30, 93.0)]\n",
"[(120, 35, 92.0)]\n",
"[(120, 40, 94.0)]\n",
"[(120, 45, 90.0)]\n",
"[(120, 50, 94.0)]\n",
"[(120, 55, 96.0)]\n",
"[(120, 60, 90.0)]\n",
"[(120, 65, 93.0)]\n",
"[(120, 70, 89.0)]\n",
"[(120, 75, 89.0)]\n",
"[(125, 5, 78.0)]\n",
"[(125, 10, 89.0)]\n",
"[(125, 15, 93.0)]\n",
"[(125, 20, 96.0)]\n",
"[(125, 25, 88.0)]\n",
"[(125, 30, 87.0)]\n",
"[(125, 35, 95.0)]\n",
"[(125, 40, 86.0)]\n",
"[(125, 45, 93.0)]\n",
"[(125, 50, 94.0)]\n",
"[(125, 55, 94.0)]\n",
"[(125, 60, 97.0)]\n",
"[(125, 65, 98.0)]\n",
"[(125, 70, 95.0)]\n",
"[(125, 75, 90.0)]\n",
"[(130, 5, 78.0)]\n",
"[(130, 10, 90.0)]\n",
"[(130, 15, 93.0)]\n",
"[(130, 20, 92.0)]\n",
"[(130, 25, 87.0)]\n",
"[(130, 30, 94.0)]\n",
"[(130, 35, 94.0)]\n",
"[(130, 40, 95.0)]\n",
"[(130, 45, 92.0)]\n",
"[(130, 50, 94.0)]\n",
"[(130, 55, 92.0)]\n",
"[(130, 60, 88.0)]\n",
"[(130, 65, 91.0)]\n",
"[(130, 70, 92.0)]\n",
"[(130, 75, 96.0)]\n",
"[(135, 5, 69.0)]\n",
"[(135, 10, 79.0)]\n",
"[(135, 15, 90.0)]\n",
"[(135, 20, 94.0)]\n",
"[(135, 25, 91.0)]\n",
"[(135, 30, 91.0)]\n",
"[(135, 35, 97.0)]\n",
"[(135, 40, 95.0)]\n",
"[(135, 45, 95.0)]\n",
"[(135, 50, 92.0)]\n",
"[(135, 55, 87.0)]\n",
"[(135, 60, 84.0)]\n",
"[(135, 65, 91.0)]\n",
"[(135, 70, 89.0)]\n",
"[(135, 75, 91.0)]\n",
"[(140, 5, 79.0)]\n",
"[(140, 10, 80.0)]\n",
"[(140, 15, 92.0)]\n",
"[(140, 20, 88.0)]\n",
"[(140, 25, 93.0)]\n",
"[(140, 30, 91.0)]\n",
"[(140, 35, 92.0)]\n",
"[(140, 40, 91.0)]\n",
"[(140, 45, 93.0)]\n",
"[(140, 50, 93.0)]\n",
"[(140, 55, 97.0)]\n",
"[(140, 60, 89.0)]\n",
"[(140, 65, 95.0)]\n",
"[(140, 70, 90.0)]\n",
"[(140, 75, 93.0)]\n",
"[(145, 5, 71.0)]\n",
"[(145, 10, 89.0)]\n",
"[(145, 15, 92.0)]\n",
"[(145, 20, 93.0)]\n",
"[(145, 25, 93.0)]\n",
"[(145, 30, 90.0)]\n",
"[(145, 35, 92.0)]\n",
"[(145, 40, 91.0)]\n",
"[(145, 45, 92.0)]\n",
"[(145, 50, 96.0)]\n",
"[(145, 55, 93.0)]\n",
"[(145, 60, 88.0)]\n",
"[(145, 65, 89.0)]\n",
"[(145, 70, 97.0)]\n",
"[(145, 75, 96.0)]\n",
"[(150, 5, 74.0)]\n",
"[(150, 10, 88.0)]\n",
"[(150, 15, 92.0)]\n",
"[(150, 20, 88.0)]\n",
"[(150, 25, 84.0)]\n",
"[(150, 30, 88.0)]\n",
"[(150, 35, 93.0)]\n",
"[(150, 40, 93.0)]\n",
"[(150, 45, 91.0)]\n",
"[(150, 50, 96.0)]\n",
"[(150, 55, 90.0)]\n",
"[(150, 60, 91.0)]\n",
"[(150, 65, 89.0)]\n",
"[(150, 70, 94.0)]\n",
"[(150, 75, 91.0)]\n",
"[(155, 5, 80.0)]\n",
"[(155, 10, 85.0)]\n",
"[(155, 15, 90.0)]\n",
"[(155, 20, 85.0)]\n",
"[(155, 25, 94.0)]\n",
"[(155, 30, 90.0)]\n",
"[(155, 35, 92.0)]\n",
"[(155, 40, 93.0)]\n",
"[(155, 45, 88.0)]\n",
"[(155, 50, 88.0)]\n",
"[(155, 55, 89.0)]\n",
"[(155, 60, 91.0)]\n",
"[(155, 65, 89.0)]\n",
"[(155, 70, 86.0)]\n",
"[(155, 75, 89.0)]\n",
"[(160, 5, 71.0)]\n",
"[(160, 10, 88.0)]\n",
"[(160, 15, 95.0)]\n",
"[(160, 20, 86.0)]\n",
"[(160, 25, 92.0)]\n",
"[(160, 30, 88.0)]\n",
"[(160, 35, 89.0)]\n",
"[(160, 40, 97.0)]\n",
"[(160, 45, 92.0)]\n",
"[(160, 50, 89.0)]\n",
"[(160, 55, 91.0)]\n",
"[(160, 60, 92.0)]\n",
"[(160, 65, 91.0)]\n",
"[(160, 70, 89.0)]\n",
"[(160, 75, 90.0)]\n",
"[(165, 5, 72.0)]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(165, 10, 83.0)]\n",
"[(165, 15, 91.0)]\n",
"[(165, 20, 86.0)]\n",
"[(165, 25, 88.0)]\n",
"[(165, 30, 85.0)]\n",
"[(165, 35, 93.0)]\n",
"[(165, 40, 93.0)]\n",
"[(165, 45, 93.0)]\n",
"[(165, 50, 95.0)]\n",
"[(165, 55, 92.0)]\n",
"[(165, 60, 87.0)]\n",
"[(165, 65, 85.0)]\n",
"[(165, 70, 97.0)]\n",
"[(165, 75, 95.0)]\n",
"[(170, 5, 76.0)]\n",
"[(170, 10, 80.0)]\n",
"[(170, 15, 91.0)]\n",
"[(170, 20, 88.0)]\n",
"[(170, 25, 87.0)]\n",
"[(170, 30, 88.0)]\n",
"[(170, 35, 91.0)]\n",
"[(170, 40, 91.0)]\n",
"[(170, 45, 90.0)]\n",
"[(170, 50, 87.0)]\n",
"[(170, 55, 89.0)]\n",
"[(170, 60, 90.0)]\n",
"[(170, 65, 89.0)]\n",
"[(170, 70, 87.0)]\n",
"[(170, 75, 98.0)]\n",
"[(175, 5, 71.0)]\n",
"[(175, 10, 85.0)]\n",
"[(175, 15, 89.0)]\n",
"[(175, 20, 87.0)]\n",
"[(175, 25, 95.0)]\n",
"[(175, 30, 91.0)]\n",
"[(175, 35, 97.0)]\n",
"[(175, 40, 92.0)]\n",
"[(175, 45, 86.0)]\n",
"[(175, 50, 90.0)]\n",
"[(175, 55, 94.0)]\n",
"[(175, 60, 86.0)]\n",
"[(175, 65, 95.0)]\n",
"[(175, 70, 98.0)]\n",
"[(175, 75, 92.0)]\n",
"[(180, 5, 67.0)]\n",
"[(180, 10, 77.0)]\n",
"[(180, 15, 95.0)]\n",
"[(180, 20, 91.0)]\n",
"[(180, 25, 88.0)]\n",
"[(180, 30, 90.0)]\n",
"[(180, 35, 91.0)]\n",
"[(180, 40, 89.0)]\n",
"[(180, 45, 85.0)]\n",
"[(180, 50, 92.0)]\n",
"[(180, 55, 90.0)]\n",
"[(180, 60, 87.0)]\n",
"[(180, 65, 93.0)]\n",
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"[(260, 50, 86.0)]\n",
"[(260, 55, 88.0)]\n",
"[(260, 60, 86.0)]\n",
"[(260, 65, 89.0)]\n",
"[(260, 70, 90.0)]\n",
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"[(265, 65, 83.0)]\n",
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"[(275, 60, 85.0)]\n",
"[(275, 65, 81.0)]\n",
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"[(275, 75, 86.0)]\n",
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"[(295, 60, 82.0)]\n",
"[(295, 65, 85.0)]\n",
"[(295, 70, 88.0)]\n",
"[(295, 75, 88.0)]\n",
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"[(300, 60, 72.0)]\n",
"[(300, 65, 80.0)]\n",
"[(300, 70, 87.0)]\n",
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"[(315, 5, 57.0)]\n",
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"[(315, 20, 84.0)]\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(315, 70, 85.0)]\n",
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"[(360, 65, 82.0)]\n",
"[(360, 70, 89.0)]\n",
"[(360, 75, 73.0)]\n",
"[(365, 5, 50.0)]\n",
"[(365, 10, 66.0)]\n",
"[(365, 15, 85.0)]\n",
"[(365, 20, 70.0)]\n",
"[(365, 25, 80.0)]\n",
"[(365, 30, 80.0)]\n",
"[(365, 35, 79.0)]\n",
"[(365, 40, 89.0)]\n",
"[(365, 45, 74.0)]\n",
"[(365, 50, 83.0)]\n",
"[(365, 55, 78.0)]\n",
"[(365, 60, 78.0)]\n",
"[(365, 65, 71.0)]\n",
"[(365, 70, 81.0)]\n",
"[(365, 75, 80.0)]\n",
"[(370, 5, 40.0)]\n",
"[(370, 10, 67.0)]\n",
"[(370, 15, 78.0)]\n",
"[(370, 20, 56.0)]\n",
"[(370, 25, 73.0)]\n",
"[(370, 30, 73.0)]\n",
"[(370, 35, 85.0)]\n",
"[(370, 40, 73.0)]\n",
"[(370, 45, 79.0)]\n",
"[(370, 50, 80.0)]\n",
"[(370, 55, 78.0)]\n",
"[(370, 60, 80.0)]\n",
"[(370, 65, 80.0)]\n",
"[(370, 70, 88.0)]\n",
"[(370, 75, 80.0)]\n",
"[(375, 5, 44.0)]\n",
"[(375, 10, 71.0)]\n",
"[(375, 15, 74.0)]\n",
"[(375, 20, 72.0)]\n",
"[(375, 25, 84.0)]\n",
"[(375, 30, 72.0)]\n",
"[(375, 35, 85.0)]\n",
"[(375, 40, 79.0)]\n",
"[(375, 45, 74.0)]\n",
"[(375, 50, 71.0)]\n",
"[(375, 55, 72.0)]\n",
"[(375, 60, 69.0)]\n",
"[(375, 65, 88.0)]\n",
"[(375, 70, 80.0)]\n",
"[(375, 75, 79.0)]\n",
"[(380, 5, 49.0)]\n",
"[(380, 10, 52.0)]\n",
"[(380, 15, 72.0)]\n",
"[(380, 20, 78.0)]\n",
"[(380, 25, 69.0)]\n",
"[(380, 30, 73.0)]\n",
"[(380, 35, 83.0)]\n",
"[(380, 40, 70.0)]\n",
"[(380, 45, 69.0)]\n",
"[(380, 50, 77.0)]\n",
"[(380, 55, 71.0)]\n",
"[(380, 60, 69.0)]\n",
"[(380, 65, 86.0)]\n",
"[(380, 70, 76.0)]\n",
"[(380, 75, 82.0)]\n",
"[(385, 5, 44.0)]\n",
"[(385, 10, 71.0)]\n",
"[(385, 15, 80.0)]\n",
"[(385, 20, 78.0)]\n",
"[(385, 25, 78.0)]\n",
"[(385, 30, 69.0)]\n",
"[(385, 35, 75.0)]\n",
"[(385, 40, 74.0)]\n",
"[(385, 45, 81.0)]\n",
"[(385, 50, 73.0)]\n",
"[(385, 55, 74.0)]\n",
"[(385, 60, 65.0)]\n",
"[(385, 65, 83.0)]\n",
"[(385, 70, 77.0)]\n",
"[(385, 75, 76.0)]\n",
"[(390, 5, 40.0)]\n",
"[(390, 10, 58.0)]\n",
"[(390, 15, 77.0)]\n",
"[(390, 20, 76.0)]\n",
"[(390, 25, 71.0)]\n",
"[(390, 30, 80.0)]\n",
"[(390, 35, 80.0)]\n",
"[(390, 40, 77.0)]\n",
"[(390, 45, 73.0)]\n",
"[(390, 50, 81.0)]\n",
"[(390, 55, 74.0)]\n",
"[(390, 60, 76.0)]\n",
"[(390, 65, 71.0)]\n",
"[(390, 70, 75.0)]\n",
"[(390, 75, 78.0)]\n",
"[(395, 5, 42.0)]\n",
"[(395, 10, 56.0)]\n",
"[(395, 15, 72.0)]\n",
"[(395, 20, 65.0)]\n",
"[(395, 25, 74.0)]\n",
"[(395, 30, 75.0)]\n",
"[(395, 35, 79.0)]\n",
"[(395, 40, 83.0)]\n",
"[(395, 45, 76.0)]\n",
"[(395, 50, 76.0)]\n",
"[(395, 55, 78.0)]\n",
"[(395, 60, 78.0)]\n",
"[(395, 65, 79.0)]\n",
"[(395, 70, 79.0)]\n",
"[(395, 75, 81.0)]\n",
"[(400, 5, 43.0)]\n",
"[(400, 10, 55.0)]\n",
"[(400, 15, 81.0)]\n",
"[(400, 20, 68.0)]\n",
"[(400, 25, 76.0)]\n",
"[(400, 30, 69.0)]\n",
"[(400, 35, 77.0)]\n",
"[(400, 40, 79.0)]\n",
"[(400, 45, 75.0)]\n",
"[(400, 50, 72.0)]\n",
"[(400, 55, 78.0)]\n",
"[(400, 60, 70.0)]\n",
"[(400, 65, 80.0)]\n",
"[(400, 70, 79.0)]\n",
"[(400, 75, 80.0)]\n",
"[(405, 5, 30.0)]\n",
"[(405, 10, 64.0)]\n",
"[(405, 15, 75.0)]\n",
"[(405, 20, 78.0)]\n",
"[(405, 25, 79.0)]\n",
"[(405, 30, 75.0)]\n",
"[(405, 35, 79.0)]\n",
"[(405, 40, 73.0)]\n",
"[(405, 45, 68.0)]\n",
"[(405, 50, 79.0)]\n",
"[(405, 55, 74.0)]\n",
"[(405, 60, 78.0)]\n",
"[(405, 65, 77.0)]\n",
"[(405, 70, 76.0)]\n",
"[(405, 75, 73.0)]\n",
"[(410, 5, 34.0)]\n",
"[(410, 10, 54.0)]\n",
"[(410, 15, 77.0)]\n",
"[(410, 20, 66.0)]\n",
"[(410, 25, 81.0)]\n",
"[(410, 30, 72.0)]\n",
"[(410, 35, 78.0)]\n",
"[(410, 40, 83.0)]\n",
"[(410, 45, 67.0)]\n",
"[(410, 50, 79.0)]\n",
"[(410, 55, 79.0)]\n",
"[(410, 60, 81.0)]\n",
"[(410, 65, 80.0)]\n",
"[(410, 70, 75.0)]\n",
"[(410, 75, 73.0)]\n",
"[(415, 5, 35.0)]\n",
"[(415, 10, 66.0)]\n",
"[(415, 15, 78.0)]\n",
"[(415, 20, 70.0)]\n",
"[(415, 25, 64.0)]\n",
"[(415, 30, 71.0)]\n",
"[(415, 35, 76.0)]\n",
"[(415, 40, 83.0)]\n",
"[(415, 45, 68.0)]\n",
"[(415, 50, 73.0)]\n",
"[(415, 55, 87.0)]\n",
"[(415, 60, 76.0)]\n",
"[(415, 65, 80.0)]\n",
"[(415, 70, 83.0)]\n",
"[(415, 75, 70.0)]\n",
"[(420, 5, 17.0)]\n",
"[(420, 10, 59.0)]\n",
"[(420, 15, 83.0)]\n",
"[(420, 20, 79.0)]\n",
"[(420, 25, 71.0)]\n",
"[(420, 30, 76.0)]\n",
"[(420, 35, 79.0)]\n",
"[(420, 40, 82.0)]\n",
"[(420, 45, 68.0)]\n",
"[(420, 50, 74.0)]\n",
"[(420, 55, 78.0)]\n",
"[(420, 60, 77.0)]\n",
"[(420, 65, 81.0)]\n",
"[(420, 70, 79.0)]\n",
"[(420, 75, 76.0)]\n",
"[(425, 5, 42.0)]\n",
"[(425, 10, 56.0)]\n",
"[(425, 15, 67.0)]\n",
"[(425, 20, 74.0)]\n",
"[(425, 25, 69.0)]\n",
"[(425, 30, 69.0)]\n",
"[(425, 35, 76.0)]\n",
"[(425, 40, 74.0)]\n",
"[(425, 45, 70.0)]\n",
"[(425, 50, 73.0)]\n",
"[(425, 55, 66.0)]\n",
"[(425, 60, 76.0)]\n",
"[(425, 65, 69.0)]\n",
"[(425, 70, 83.0)]\n",
"[(425, 75, 81.0)]\n",
"[(430, 5, 16.0)]\n",
"[(430, 10, 55.0)]\n",
"[(430, 15, 72.0)]\n",
"[(430, 20, 72.0)]\n",
"[(430, 25, 79.0)]\n",
"[(430, 30, 81.0)]\n",
"[(430, 35, 77.0)]\n",
"[(430, 40, 78.0)]\n",
"[(430, 45, 66.0)]\n",
"[(430, 50, 73.0)]\n",
"[(430, 55, 74.0)]\n",
"[(430, 60, 85.0)]\n",
"[(430, 65, 66.0)]\n",
"[(430, 70, 75.0)]\n",
"[(430, 75, 70.0)]\n",
"[(435, 5, 24.0)]\n",
"[(435, 10, 49.0)]\n",
"[(435, 15, 66.0)]\n",
"[(435, 20, 71.0)]\n",
"[(435, 25, 79.0)]\n",
"[(435, 30, 70.0)]\n",
"[(435, 35, 70.0)]\n",
"[(435, 40, 81.0)]\n",
"[(435, 45, 69.0)]\n",
"[(435, 50, 69.0)]\n",
"[(435, 55, 73.0)]\n",
"[(435, 60, 66.0)]\n",
"[(435, 65, 82.0)]\n",
"[(435, 70, 76.0)]\n",
"[(435, 75, 74.0)]\n",
"[(440, 5, 28.0)]\n",
"[(440, 10, 70.0)]\n",
"[(440, 15, 81.0)]\n",
"[(440, 20, 67.0)]\n",
"[(440, 25, 74.0)]\n",
"[(440, 30, 70.0)]\n",
"[(440, 35, 75.0)]\n",
"[(440, 40, 78.0)]\n",
"[(440, 45, 62.0)]\n",
"[(440, 50, 66.0)]\n",
"[(440, 55, 80.0)]\n",
"[(440, 60, 76.0)]\n",
"[(440, 65, 76.0)]\n",
"[(440, 70, 80.0)]\n",
"[(440, 75, 65.0)]\n",
"[(445, 5, 11.0)]\n",
"[(445, 10, 54.0)]\n",
"[(445, 15, 66.0)]\n",
"[(445, 20, 68.0)]\n",
"[(445, 25, 72.0)]\n",
"[(445, 30, 80.0)]\n",
"[(445, 35, 78.0)]\n",
"[(445, 40, 74.0)]\n",
"[(445, 45, 69.0)]\n",
"[(445, 50, 84.0)]\n",
"[(445, 55, 77.0)]\n",
"[(445, 60, 75.0)]\n",
"[(445, 65, 76.0)]\n",
"[(445, 70, 76.0)]\n",
"[(445, 75, 78.0)]\n",
"[(450, 5, 27.0)]\n",
"[(450, 10, 42.0)]\n",
"[(450, 15, 70.0)]\n",
"[(450, 20, 69.0)]\n",
"[(450, 25, 76.0)]\n",
"[(450, 30, 68.0)]\n",
"[(450, 35, 71.0)]\n",
"[(450, 40, 78.0)]\n",
"[(450, 45, 56.0)]\n",
"[(450, 50, 76.0)]\n",
"[(450, 55, 74.0)]\n",
"[(450, 60, 74.0)]\n",
"[(450, 65, 74.0)]\n",
"[(450, 70, 76.0)]\n",
"[(450, 75, 75.0)]\n",
"[(455, 5, 23.0)]\n",
"[(455, 10, 49.0)]\n",
"[(455, 15, 77.0)]\n",
"[(455, 20, 66.0)]\n",
"[(455, 25, 72.0)]\n",
"[(455, 30, 68.0)]\n",
"[(455, 35, 75.0)]\n",
"[(455, 40, 72.0)]\n",
"[(455, 45, 68.0)]\n",
"[(455, 50, 70.0)]\n",
"[(455, 55, 74.0)]\n",
"[(455, 60, 75.0)]\n",
"[(455, 65, 72.0)]\n",
"[(455, 70, 69.0)]\n",
"[(455, 75, 77.0)]\n",
"[(460, 5, 18.0)]\n",
"[(460, 10, 60.0)]\n",
"[(460, 15, 70.0)]\n",
"[(460, 20, 65.0)]\n",
"[(460, 25, 75.0)]\n",
"[(460, 30, 65.0)]\n",
"[(460, 35, 69.0)]\n",
"[(460, 40, 82.0)]\n",
"[(460, 45, 68.0)]\n",
"[(460, 50, 71.0)]\n",
"[(460, 55, 79.0)]\n",
"[(460, 60, 67.0)]\n",
"[(460, 65, 69.0)]\n",
"[(460, 70, 78.0)]\n",
"[(460, 75, 68.0)]\n",
"[(465, 5, 31.0)]\n",
"[(465, 10, 51.0)]\n",
"[(465, 15, 79.0)]\n",
"[(465, 20, 63.0)]\n",
"[(465, 25, 71.0)]\n",
"[(465, 30, 69.0)]\n",
"[(465, 35, 71.0)]\n",
"[(465, 40, 74.0)]\n",
"[(465, 45, 74.0)]\n",
"[(465, 50, 75.0)]\n",
"[(465, 55, 77.0)]\n",
"[(465, 60, 72.0)]\n",
"[(465, 65, 67.0)]\n",
"[(465, 70, 74.0)]\n",
"[(465, 75, 79.0)]\n",
"[(470, 5, 21.0)]\n",
"[(470, 10, 53.0)]\n",
"[(470, 15, 75.0)]\n",
"[(470, 20, 64.0)]\n",
"[(470, 25, 70.0)]\n",
"[(470, 30, 67.0)]\n",
"[(470, 35, 73.0)]\n",
"[(470, 40, 74.0)]\n",
"[(470, 45, 68.0)]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(470, 50, 70.0)]\n",
"[(470, 55, 69.0)]\n",
"[(470, 60, 60.0)]\n",
"[(470, 65, 75.0)]\n",
"[(470, 70, 73.0)]\n",
"[(470, 75, 84.0)]\n",
"[(475, 5, 26.0)]\n",
"[(475, 10, 57.0)]\n",
"[(475, 15, 75.0)]\n",
"[(475, 20, 60.0)]\n",
"[(475, 25, 68.0)]\n",
"[(475, 30, 61.0)]\n",
"[(475, 35, 73.0)]\n",
"[(475, 40, 70.0)]\n",
"[(475, 45, 76.0)]\n",
"[(475, 50, 64.0)]\n",
"[(475, 55, 73.0)]\n",
"[(475, 60, 81.0)]\n",
"[(475, 65, 69.0)]\n",
"[(475, 70, 77.0)]\n",
"[(475, 75, 64.0)]\n",
"[(480, 5, 10.0)]\n",
"[(480, 10, 44.0)]\n",
"[(480, 15, 66.0)]\n",
"[(480, 20, 57.0)]\n",
"[(480, 25, 72.0)]\n",
"[(480, 30, 70.0)]\n",
"[(480, 35, 63.0)]\n",
"[(480, 40, 73.0)]\n",
"[(480, 45, 66.0)]\n",
"[(480, 50, 81.0)]\n",
"[(480, 55, 75.0)]\n",
"[(480, 60, 55.0)]\n",
"[(480, 65, 67.0)]\n",
"[(480, 70, 79.0)]\n",
"[(480, 75, 75.0)]\n",
"[(485, 5, 8.0)]\n",
"[(485, 10, 51.0)]\n",
"[(485, 15, 69.0)]\n",
"[(485, 20, 71.0)]\n",
"[(485, 25, 67.0)]\n",
"[(485, 30, 73.0)]\n",
"[(485, 35, 63.0)]\n",
"[(485, 40, 82.0)]\n",
"[(485, 45, 71.0)]\n",
"[(485, 50, 72.0)]\n",
"[(485, 55, 74.0)]\n",
"[(485, 60, 68.0)]\n",
"[(485, 65, 75.0)]\n",
"[(485, 70, 77.0)]\n",
"[(485, 75, 71.0)]\n",
"[(490, 5, 8.0)]\n",
"[(490, 10, 57.0)]\n",
"[(490, 15, 77.0)]\n",
"[(490, 20, 60.0)]\n",
"[(490, 25, 65.0)]\n",
"[(490, 30, 67.0)]\n",
"[(490, 35, 70.0)]\n",
"[(490, 40, 75.0)]\n",
"[(490, 45, 74.0)]\n",
"[(490, 50, 70.0)]\n",
"[(490, 55, 69.0)]\n",
"[(490, 60, 65.0)]\n",
"[(490, 65, 79.0)]\n",
"[(490, 70, 71.0)]\n",
"[(490, 75, 76.0)]\n",
"[(495, 5, 23.0)]\n",
"[(495, 10, 43.0)]\n",
"[(495, 15, 75.0)]\n",
"[(495, 20, 71.0)]\n",
"[(495, 25, 71.0)]\n",
"[(495, 30, 67.0)]\n",
"[(495, 35, 68.0)]\n",
"[(495, 40, 69.0)]\n",
"[(495, 45, 65.0)]\n",
"[(495, 50, 65.0)]\n",
"[(495, 55, 67.0)]\n",
"[(495, 60, 68.0)]\n",
"[(495, 65, 79.0)]\n",
"[(495, 70, 75.0)]\n",
"[(495, 75, 70.0)]\n"
]
}
],
"source": [
"print('population mean: {} popoulation SD: {}'.format(m,s))\n",
"print('[N, n, accuracy]')\n",
"\n",
"N_list = range(5,500,5)\n",
"n_list = range(5,80,5) # different sample sizes\n",
"\n",
"for each_N in N_list:\n",
" for each_n in n_list:\n",
" print(repeated_experiments_with_CI_2(population,m,s,[each_N],[each_n],2,0,'_'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are now poor performances in accuracy as seen in above result.. trying to visualize how many.. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1dce676ca20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def get_mode_label(mode):\n",
" mode_labels={\n",
" 1:\"population SD\",\n",
" 2:\"unbiased sample SD\",\n",
" 3:\"biased sample SD\",\n",
" 4:\"Wilson Score method\"\n",
" }\n",
" return mode_labels.get(mode, \"invalid mode\")\n",
"\n",
"def get_dist_label(dist):\n",
" dist_labels={\n",
" 0:\"Z distribution\",\n",
" 1:\"T distribution\"\n",
" }\n",
" return dist_labels.get(dist, \"invalid dist\")\n",
"\n",
"def plot_accuracy(ax, accuracy_list):\n",
" x,y,z=zip(*accuracy_list) \n",
" labels = ['$< 95\\%$', '$ \\geq 95\\%$']\n",
" colors = ['C1','green']\n",
" for xi, yi, zi in zip(x,y,z):\n",
" if zi == 1: \n",
" s = ax.scatter(yi, xi, c=colors[zi], label=labels[zi], s=20, edgecolors='None', alpha=0.75)\n",
" else:\n",
" f = ax.scatter(yi, xi, c=colors[zi], label=labels[zi], s=20, edgecolors='None', alpha=0.75)\n",
"\n",
" ax.set_ylabel('Experiment Size $N$')\n",
" ax.set_xlabel('Sample Size $n$')\n",
" #ax.legend((s,f),(labels[1],labels[0]), loc='lower right', scatterpoints=1)\n",
"\n",
" xmin = 0\n",
" xmax = ax.get_xlim()[1]\n",
" from math import ceil,floor\n",
" xminint = floor(xmin)\n",
" xmaxint = ceil(xmax)\n",
" xint = range(xminint, xmaxint, 5)\n",
" ax.set_xticks(xint)\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(1,1, figsize=(5,7))\n",
"mode = 2\n",
"dist = 0\n",
"for each_N in N_list:\n",
" for each_n in n_list:\n",
" accuracy_list = repeated_experiments_with_CI_2(population,m,s,[each_N],[each_n],mode,dist,'b')\n",
" plot_accuracy(ax, accuracy_list)\n",
"\n",
"ax.set_title('Using {} and {}'.format(get_dist_label(dist), get_mode_label(mode)))\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Result: <font color='red'>Failed</font> \n",
"\n",
"Why would this happen? :("
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:Anaconda3]",
"language": "python",
"name": "conda-env-Anaconda3-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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