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joshiga · May 16, 2015 · 3871c6f · 3871c6f
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diff --git a/.ipynb_checkpoints/Chi-Square-checkpoint.ipynb b/.ipynb_checkpoints/Chi-Square-checkpoint.ipynb
@@ -1050,6 +1050,8 @@
  "source": [
  "stats.chisquare returns two values, the chi-squared test statistic and the p-value of the test.\n",
  "\n",
+ "With such a high p-value, we have no reason to doubt the fairness of the dice.\n",
+ "\n",
  "That's it for the Chi-Square Distirbution and Test!\n",
  "\n",
  "For more information, check out these links:\n",

diff --git a/.ipynb_checkpoints/Poisson Distribution-checkpoint.ipynb b/.ipynb_checkpoints/Poisson Distribution-checkpoint.ipynb
@@ -12,7 +12,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "A poisson distribution focuses on the number of discrete events or occurrences over a specified interval or continuum (e.g. time,length,distance,etc.). We'll look at the formal difintion, get a break down of what that actually means, see an example and then look at the other characteristics such as mean and standard deviation."
+ "A poisson distribution focuses on the number of discrete events or occurrences over a specified interval or continuum (e.g. time,length,distance,etc.). We'll look at the formal definition, get a break down of what that actually means, see an example and then look at the other characteristics such as mean and standard deviation."
  ]
  },
  {
@@ -324,7 +324,7 @@
  "source": [
  "For the question: What is the probability that more than 10 customers arrive? We need to sum up the value of every bar past 10 the 10 customers bar.\n",
  "\n",
- "We can do this by using a Cumulative Distirbution Function (CDF). This describes the probability that a random variable X with a given probability distribution (such as the Poisson in this current case) will be found to have a value less than or equal to X.\n",
+ "We can do this by using a Cumulative Distribution Function (CDF). This describes the probability that a random variable X with a given probability distribution (such as the Poisson in this current case) will be found to have a value less than or equal to X.\n",
  "\n",
  "What this means is if we use the CDF to calcualte the probability of 10 or less customers showing up we can take that probability and subtract it from the total probability space, which is just 1 (the sum of all the probabilities for every number of customers)."
  ]

diff --git a/Chi-Square.ipynb b/Chi-Square.ipynb
@@ -1050,6 +1050,8 @@
  "source": [
  "stats.chisquare returns two values, the chi-squared test statistic and the p-value of the test.\n",
  "\n",
+ "With such a high p-value, we have no reason to doubt the fairness of the dice.\n",
+ "\n",
  "That's it for the Chi-Square Distirbution and Test!\n",
  "\n",
  "For more information, check out these links:\n",

diff --git a/Poisson Distribution.ipynb b/Poisson Distribution.ipynb
@@ -12,7 +12,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "A poisson distribution focuses on the number of discrete events or occurrences over a specified interval or continuum (e.g. time,length,distance,etc.). We'll look at the formal difintion, get a break down of what that actually means, see an example and then look at the other characteristics such as mean and standard deviation."
+ "A poisson distribution focuses on the number of discrete events or occurrences over a specified interval or continuum (e.g. time,length,distance,etc.). We'll look at the formal definition, get a break down of what that actually means, see an example and then look at the other characteristics such as mean and standard deviation."
  ]
  },
  {
@@ -324,7 +324,7 @@
  "source": [
  "For the question: What is the probability that more than 10 customers arrive? We need to sum up the value of every bar past 10 the 10 customers bar.\n",
  "\n",
- "We can do this by using a Cumulative Distirbution Function (CDF). This describes the probability that a random variable X with a given probability distribution (such as the Poisson in this current case) will be found to have a value less than or equal to X.\n",
+ "We can do this by using a Cumulative Distribution Function (CDF). This describes the probability that a random variable X with a given probability distribution (such as the Poisson in this current case) will be found to have a value less than or equal to X.\n",
  "\n",
  "What this means is if we use the CDF to calcualte the probability of 10 or less customers showing up we can take that probability and subtract it from the total probability space, which is just 1 (the sum of all the probabilities for every number of customers)."
  ]

diff --git a/Untitled.ipynb b/Untitled.ipynb
@@ -1,5 +1,16 @@
 {
  "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import math"
+ ]
+ },
  {
  "cell_type": "code",
  "execution_count": 3,
@@ -10,215 +21,16 @@
  {
  "data": {
  "text/plain": [
- "<matplotlib.collections.PolyCollection at 0x15c60048>"
+ "0.1353352832366127"
  ]
  },
  "execution_count": 3,
  "metadata": {},
  "output_type": "execute_result"
- },
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x3ff66a0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
  }
  ],
  "source": [
- "#Import\n",
- "import numpy as np\n",
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "#Import the stats library\n",
- "from scipy import stats\n",
- "\n",
- "# Set the mean\n",
- "mean = 0\n",
- "\n",
- "#Set the standard deviation\n",
- "std = 1\n",
- "\n",
- "\n",
- "# Create a range\n",
- "X = np.arange(-4,4,0.01)\n",
- "\n",
- "#Create the normal distribution for the range\n",
- "Y = stats.norm.pdf(X,mean,std)\n",
- "\n",
- "#\n",
- "plt.plot(X,Y)\n",
- "x_fill = np.linspace(-1,1,.01)\n",
- "y_fill = stats.norm.pdf(x_fill,mean,std)\n",
- "plt.fill_between(y_fill,x_fill,0)"
+ "math.exp(-2)"
  ]
  },
  {