parthi2929

parthi2929

# SDSP = Sample Distribution of Sample Proportions
# This is helper file for programmatic illustrations of SDSP concepts. 

from random import shuffle
import pandas as pd
import numpy as np
from math import sqrt, pi

def create_bernoulli_population(N, p):
    """

parthi2929

parthi2929

parthi2929

from random import shuffle 
import matplotlib.pyplot as plt
from SDSPSM import get_metrics
from random import choices, seed
from math import sqrt, pi

def create_bernoulli_population(T, p):
    y_freq = int(p*T)                 
    y_pops = [1]*y_freq        
    o_freq = int((1-p)*T)

parthi2929

parthi2929

{
 "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",

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sourcefile = r"../../test.ipynb"  # file to be converted

with open(sourcefile) as nb_file:
    nb_contents = nb_file.read()
    
import nbformat
# Convert using the ordinary exporter
notebook = nbformat.reads(nb_contents, as_version=4)

# create a configuration object to extract figures as files

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Population"
   ]
  },
  {

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try converting this page to latex and compile using pdflatex..\n",
    "\n",
    "\\begin{equation}\n",
    "\\color {blue}{\n",