moorepants/eme150a_fall_2015_stats.ipynb

## eme150a_fall_2015_stats.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EME 150A Fall 2015 Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import probplot\n",
    "import statsmodels.api as sm\n",
    "from IPython.core.pylabtools import figsize\n",
    "%matplotlib inline\n",
    "figsize(8.0, 8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Midterm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "midterm_points = pd.read_csv('midterm-grades.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# anonymize\n",
    "if 'Name' in midterm_points:\n",
    "    del midterm_points['Name']\n",
    "if 'ID' in midterm_points:\n",
    "    del midterm_points['ID']\n",
    "#grades.to_csv('midterm-grades.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TF1</th>\n",
       "      <th>TF2</th>\n",
       "      <th>TF3</th>\n",
       "      <th>TF4</th>\n",
       "      <th>TF5</th>\n",
       "      <th>TF6</th>\n",
       "      <th>TF7</th>\n",
       "      <th>TF8</th>\n",
       "      <th>TF9</th>\n",
       "      <th>TF10</th>\n",
       "      <th>...</th>\n",
       "      <th>SA1</th>\n",
       "      <th>SA2</th>\n",
       "      <th>SA3</th>\n",
       "      <th>SA4</th>\n",
       "      <th>Mohr</th>\n",
       "      <th>Multiaxial</th>\n",
       "      <th>Beam Deflect</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Total</th>\n",
       "      <th>Hrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>62.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TF1  TF2  TF3  TF4  TF5  TF6  TF7  TF8  TF9  TF10 ...   SA1  SA2  SA3  SA4  \\\n",
       "0    0  0.0  1.0    1    1  1.0    1  1.0    0   1.0 ...     0    5    5    0   \n",
       "1    0  0.5  0.5    1    1  0.5    1  0.5    0   1.0 ...     5    5    0    5   \n",
       "2    0  0.5  0.5    1    1  0.5    1  0.5    0   1.0 ...     5    5    5    3   \n",
       "3    0  0.5  0.5    1    1  0.5    1  0.5    0   0.5 ...     5    5    0    5   \n",
       "4    0  0.0  0.0    1    1  1.0    1  1.0    1   1.0 ...     5    0    0    3   \n",
       "\n",
       "   Mohr  Multiaxial  Beam Deflect  Bonus  Total  Hrs  \n",
       "0    14           9            11      2   57.0  NaN  \n",
       "1    18          10             8      2   61.0    9  \n",
       "2    16           9             9      2   62.0   10  \n",
       "3    20          11             5      2   62.5    5  \n",
       "4    18          13             8      2   64.0    8  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What was the overall grade distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     77.148148\n",
       "std      11.462996\n",
       "min      57.000000\n",
       "25%      66.500000\n",
       "50%      80.000000\n",
       "75%      86.000000\n",
       "max      95.000000\n",
       "Name: Total, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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l71/2+jKfT3q3exi6AAAshJ1uAPY1iMLZmwN9mgM7XQAABsbQ7cDeZW7Z+5e9vsznk97t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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a2beb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = midterm_points['Total'].hist(bins=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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47z0YNgx23TWskrOWtqkVEZGsk9T2q5WV0LFjMJp+0iSFfJop6EVEJDzPPw8HHQQXXxys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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a22f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = probplot(midterm_points['Total'], dist=\"norm\", plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'][midterm_points['Total'] >= 90.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'][(midterm_points['Total'] >= 80.0) & (midterm_points['Total'] < 90.0)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'][(midterm_points['Total'] >= 70.0) & (midterm_points['Total'] < 80.0)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'][(midterm_points['Total'] >= 60.0) & (midterm_points['Total'] < 70.0)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Total'][midterm_points['Total'] < 60.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## True/False Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TF1</th>\n",
       "      <th>TF2</th>\n",
       "      <th>TF3</th>\n",
       "      <th>TF4</th>\n",
       "      <th>TF5</th>\n",
       "      <th>TF6</th>\n",
       "      <th>TF7</th>\n",
       "      <th>TF8</th>\n",
       "      <th>TF9</th>\n",
       "      <th>TF10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.981481</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.870370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.478899</td>\n",
       "      <td>0.373632</td>\n",
       "      <td>0.348991</td>\n",
       "      <td>0.096225</td>\n",
       "      <td>0.395847</td>\n",
       "      <td>0.232661</td>\n",
       "      <td>0.192450</td>\n",
       "      <td>0.376481</td>\n",
       "      <td>0.459933</td>\n",
       "      <td>0.327948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TF1        TF2        TF3        TF4        TF5        TF6  \\\n",
       "count  27.000000  27.000000  27.000000  27.000000  27.000000  27.000000   \n",
       "mean    0.462963   0.703704   0.777778   0.981481   0.814815   0.851852   \n",
       "std     0.478899   0.373632   0.348991   0.096225   0.395847   0.232661   \n",
       "min     0.000000   0.000000   0.000000   0.500000   0.000000   0.500000   \n",
       "25%     0.000000   0.500000   0.500000   1.000000   1.000000   0.500000   \n",
       "50%     0.500000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "75%     1.000000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "max     1.000000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "\n",
       "             TF7        TF8        TF9       TF10  \n",
       "count  27.000000  27.000000  27.000000  27.000000  \n",
       "mean    0.962963   0.759259   0.333333   0.870370  \n",
       "std     0.192450   0.376481   0.459933   0.327948  \n",
       "min     0.000000   0.000000   0.000000   0.000000  \n",
       "25%     1.000000   0.500000   0.000000   1.000000  \n",
       "50%     1.000000   1.000000   0.000000   1.000000  \n",
       "75%     1.000000   1.000000   1.000000   1.000000  \n",
       "max     1.000000   1.000000   1.000000   1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_points = midterm_points[[x for x in midterm_points.columns if x.startswith('TF')]]\n",
    "tf_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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slH7VYErPk7Sq05vlqsW+lrQtcJeZvaJLPm+UK4Q3yk4ZqFqj3I1+1aA3yr2TpPv6ZcBj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\nR0TEjjHe7zXlPuI1ZTOz6oqcKa8Blra/KOkY4FXAg6krZWZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a0c1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = tf_points.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But what if we count the 0.5 values as 1.0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_points2 = tf_points.copy()\n",
    "tf_points2[tf_points2 == 0.5] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TF1</th>\n",
       "      <th>TF2</th>\n",
       "      <th>TF3</th>\n",
       "      <th>TF4</th>\n",
       "      <th>TF5</th>\n",
       "      <th>TF6</th>\n",
       "      <th>TF7</th>\n",
       "      <th>TF8</th>\n",
       "      <th>TF9</th>\n",
       "      <th>TF10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>1</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.509175</td>\n",
       "      <td>0.362014</td>\n",
       "      <td>0.320256</td>\n",
       "      <td>0</td>\n",
       "      <td>0.395847</td>\n",
       "      <td>0</td>\n",
       "      <td>0.192450</td>\n",
       "      <td>0.362014</td>\n",
       "      <td>0.492103</td>\n",
       "      <td>0.320256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TF1        TF2        TF3  TF4        TF5  TF6        TF7  \\\n",
       "count  27.000000  27.000000  27.000000   27  27.000000   27  27.000000   \n",
       "mean    0.518519   0.851852   0.888889    1   0.814815    1   0.962963   \n",
       "std     0.509175   0.362014   0.320256    0   0.395847    0   0.192450   \n",
       "min     0.000000   0.000000   0.000000    1   0.000000    1   0.000000   \n",
       "25%     0.000000   1.000000   1.000000    1   1.000000    1   1.000000   \n",
       "50%     1.000000   1.000000   1.000000    1   1.000000    1   1.000000   \n",
       "75%     1.000000   1.000000   1.000000    1   1.000000    1   1.000000   \n",
       "max     1.000000   1.000000   1.000000    1   1.000000    1   1.000000   \n",
       "\n",
       "             TF8        TF9       TF10  \n",
       "count  27.000000  27.000000  27.000000  \n",
       "mean    0.851852   0.370370   0.888889  \n",
       "std     0.362014   0.492103   0.320256  \n",
       "min     0.000000   0.000000   0.000000  \n",
       "25%     1.000000   0.000000   1.000000  \n",
       "50%     1.000000   0.000000   1.000000  \n",
       "75%     1.000000   1.000000   1.000000  \n",
       "max     1.000000   1.000000   1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_points2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w5SbVpjzS09c9VYdXAysjYhtsb/PbJ08BzDKq9bR51h2+DlqVMj/oNaDqsP+/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\nZTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0650b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = tf_points2.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many people didn't write an explanation for the false statements?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_points[tf_points == 0.5].count().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Choice Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MC1</th>\n",
       "      <th>MC2</th>\n",
       "      <th>MC3</th>\n",
       "      <th>MC4</th>\n",
       "      <th>MC5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.851852</td>\n",
       "      <td>1.259259</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>1.481481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.533761</td>\n",
       "      <td>0.984206</td>\n",
       "      <td>0.984206</td>\n",
       "      <td>1.018350</td>\n",
       "      <td>0.893152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MC1        MC2        MC3        MC4        MC5\n",
       "count  27.000000  27.000000  27.000000  27.000000  27.000000\n",
       "mean    1.851852   1.259259   0.740741   0.962963   1.481481\n",
       "std     0.533761   0.984206   0.984206   1.018350   0.893152\n",
       "min     0.000000   0.000000   0.000000   0.000000   0.000000\n",
       "25%     2.000000   0.000000   0.000000   0.000000   1.000000\n",
       "50%     2.000000   2.000000   0.000000   0.000000   2.000000\n",
       "75%     2.000000   2.000000   2.000000   2.000000   2.000000\n",
       "max     2.000000   2.000000   2.000000   2.000000   2.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc_points = midterm_points[[x for x in midterm_points.columns if x.startswith('MC')]]\n",
    "mc_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZfqaAleXzZcAExSx+ANPlv6OV77rrDqanp5mcLMq9v7DqKveWNfX+4yr31yWF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a05f80978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = mc_points.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short Answer Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SA1</th>\n",
       "      <th>SA2</th>\n",
       "      <th>SA3</th>\n",
       "      <th>SA4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.259259</td>\n",
       "      <td>4.296296</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.810070</td>\n",
       "      <td>1.234592</td>\n",
       "      <td>2.253203</td>\n",
       "      <td>1.185141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SA1        SA2        SA3        SA4\n",
       "count  27.000000  27.000000  27.000000  27.000000\n",
       "mean    4.259259   4.296296   2.666667   4.407407\n",
       "std     1.810070   1.234592   2.253203   1.185141\n",
       "min     0.000000   0.000000   0.000000   0.000000\n",
       "25%     5.000000   3.000000   0.000000   4.500000\n",
       "50%     5.000000   5.000000   3.000000   5.000000\n",
       "75%     5.000000   5.000000   5.000000   5.000000\n",
       "max     5.000000   5.000000   5.000000   5.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa_points = midterm_points[[x for x in midterm_points.columns if x.startswith('SA')]]\n",
    "sa_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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FtbOpvcrrw2yTqZxtHL1uwJIkteIMWKLOTKjrrHd1QZV6dw9YkqQGRjbgiLgiIlYj4pZ1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a05ef5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sa_points.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Answer Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mohr</th>\n",
       "      <th>Multiaxial</th>\n",
       "      <th>Beam Deflect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.629630</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>14.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.633339</td>\n",
       "      <td>4.104407</td>\n",
       "      <td>4.548253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mohr  Multiaxial  Beam Deflect\n",
       "count  27.000000   27.000000     27.000000\n",
       "mean   17.629630   14.333333     14.074074\n",
       "std     2.633339    4.104407      4.548253\n",
       "min     9.000000    7.000000      5.000000\n",
       "25%    16.500000   11.000000     10.000000\n",
       "50%    18.000000   15.000000     14.000000\n",
       "75%    20.000000   18.000000     18.000000\n",
       "max    20.000000   20.000000     20.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_points = midterm_points[['Mohr', 'Multiaxial', 'Beam Deflect']]\n",
    "la_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T8+/XS9q34vhPUvb+g8fk238t6TUTxnyxpAVJNw5c9h5Jb8m/f6uksyuO/1JJ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a05ec00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = la_points.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean      1.666667\n",
       "std       0.733799\n",
       "min       0.000000\n",
       "25%       2.000000\n",
       "50%       2.000000\n",
       "75%       2.000000\n",
       "max       2.000000\n",
       "Name: Bonus, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midterm_points['Bonus'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ssmuNS3Zjk9+clDIAdMKmDMDmbMrLnJQBoBNKmVV2rXHJbmzym5NSBoBO2JQB2JxNeZmT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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a1f1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = midterm_points['Bonus'].hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which problems were missed the most? i.e. which problems averaged less that 75% of the maximum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hrs             31.597222\n",
       "TF9             33.333333\n",
       "MC3             37.037037\n",
       "TF1             46.296296\n",
       "MC4             48.148148\n",
       "SA3             53.333333\n",
       "MC2             62.962963\n",
       "TF2             70.370370\n",
       "Beam Deflect    70.370370\n",
       "Multiaxial      71.666667\n",
       "MC5             74.074074\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_correct = midterm_points.mean() / midterm_points.max() * 100.0\n",
    "percent_correct[percent_correct < 75.0].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do hours spent studying correlate to the total midterm grade?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q+r0vZ0fEKyno3C3Svl9kFeevqT+hOyKZueUI3/pnwI2DrGWDfAd48YLtF9X7\nipGZf1//+1BE7KU/RDHbbFXr6mBEnJCZB+tx0QebLmg9ZeZDCzZb/3MXEZvoh+CfZ+an691FnMPF\n2lba+QPIzP8dET3gHAo5dwstbN9hY/FHdP6G+kp+OVHmwjm3AC+LiJMj4qeAtwD7Gq5p3cQSiyA1\nW9WaBU8fI9sHvL1+Pgl8+vADWuZp7Svw5+4/A1/PzA8v2FfKOXxG20o5fxHxs4e6qiNiDNhCf95B\nEeduifbdtZrz1+bZ9dfSH2eaXzjn0FhMm9V/EvFh+r+AfTQz/7jhktZNRLwE2Eu/C+3QIkitbV9E\nfBzoAMcBB4EdwKeA64GTgHuB8zLz0aZqXIsl2vfrFPJzFxGvAz4H3EH/ezKB3we+DFxHi8/hMm07\nnwLOX0T8Y/oT646qH5/IzJ0RcSwtP3ewbPtWnHutDXlJkrS81nbXS5Kk5RnykiQVypCXJKlQhrwk\nSYUy5CVJKpQhL0lSoQx5SU8TEY8ftj0ZEX/aVD2SVs+Ql3S4xRbPeMa++haYkobYUK9dL2m4RMQ1\nwP8FXg3MRsQ++is0HlpV7Z9m5v9psERJCxjykg53dETcWj8PYDNPv4fCCzPztQB1yL8jM78YEUfT\n/wVA0pAw5CUd7on6dsBAf0we+KUFr1+/4PkXgA9FxMeAv8rMou6aKLWdY/KSVmq+Oz4zdwEXAmPA\nFyLi5Y1VJekZvJKXdLj4yW+p3xhxSmbeCdwZEWcBpwLfHFhlklbEK3lJh1vu1pSHv/Z7EXFHRNwO\n/APw3wZXlqSV8lazkiQVyit5SZIKZchLklQoQ16SpEIZ8pIkFcqQlySpUIa8JEmFMuQlSSrU/wNL\naZHGKJsvvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0601b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = midterm_points.plot(kind='scatter', x='Hrs', y='Total')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doesn't look like it, so further analysis will be skipped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Exam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_points = pd.read_csv('final-grades.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# anonymize\n",
    "if 'Name' in final_points:\n",
    "    del final_points['Name']\n",
    "if 'ID' in midterm_points:\n",
    "    del final_points['ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What was the overall grade distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     76.740741\n",
       "std      12.870935\n",
       "min      46.000000\n",
       "25%      71.000000\n",
       "50%      77.000000\n",
       "75%      84.500000\n",
       "max      98.000000\n",
       "Name: Total, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_points['Total'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7QBLVHSANzr8xThx2hYi4StLHJF25vP5vtNb+2XEHAwBgk3V1wBFxsrX2dERcLul3Jd3d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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a05fe6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = final_points['Total'].hist(bins=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FratfTtcs+VvFgsyVJSK5an1+5d9cxu8Zw+GM5v11Z3OKLp0TCU1tI/oKYFry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\nM7OMOdGbmZllzInezMwsY070ZmZmGfsbt1rDyKVGVfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a06099a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = probplot(final_points['Total'], dist=\"norm\", plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TF1</th>\n",
       "      <th>TF2</th>\n",
       "      <th>TF3</th>\n",
       "      <th>TF4</th>\n",
       "      <th>TF5</th>\n",
       "      <th>TF6</th>\n",
       "      <th>TF7</th>\n",
       "      <th>TF8</th>\n",
       "      <th>TF9</th>\n",
       "      <th>TF10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.407407</td>\n",
       "      <td>1.851852</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.481481</td>\n",
       "      <td>1.740741</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.296296</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.629630</td>\n",
       "      <td>1.185185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.930643</td>\n",
       "      <td>0.533761</td>\n",
       "      <td>0.751068</td>\n",
       "      <td>0.893152</td>\n",
       "      <td>0.594371</td>\n",
       "      <td>1.012739</td>\n",
       "      <td>0.953327</td>\n",
       "      <td>1.012739</td>\n",
       "      <td>0.687702</td>\n",
       "      <td>1.001423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TF1        TF2        TF3        TF4        TF5        TF6  \\\n",
       "count  27.000000  27.000000  27.000000  27.000000  27.000000  27.000000   \n",
       "mean    1.407407   1.851852   1.555556   1.481481   1.740741   0.888889   \n",
       "std     0.930643   0.533761   0.751068   0.893152   0.594371   1.012739   \n",
       "min     0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%     0.000000   2.000000   1.000000   1.000000   2.000000   0.000000   \n",
       "50%     2.000000   2.000000   2.000000   2.000000   2.000000   0.000000   \n",
       "75%     2.000000   2.000000   2.000000   2.000000   2.000000   2.000000   \n",
       "max     2.000000   2.000000   2.000000   2.000000   2.000000   2.000000   \n",
       "\n",
       "             TF7        TF8        TF9       TF10  \n",
       "count  27.000000  27.000000  27.000000  27.000000  \n",
       "mean    1.296296   0.888889   1.629630   1.185185  \n",
       "std     0.953327   1.012739   0.687702   1.001423  \n",
       "min     0.000000   0.000000   0.000000   0.000000  \n",
       "25%     0.000000   0.000000   1.500000   0.000000  \n",
       "50%     2.000000   0.000000   2.000000   2.000000  \n",
       "75%     2.000000   2.000000   2.000000   2.000000  \n",
       "max     2.000000   2.000000   2.000000   2.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_points = final_points[[x for x in final_points.columns if x.startswith('TF')]]\n",
    "tf_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Castifliango</th>\n",
       "      <th>Buckling</th>\n",
       "      <th>Static</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.444444</td>\n",
       "      <td>14.592593</td>\n",
       "      <td>15.259259</td>\n",
       "      <td>16.777778</td>\n",
       "      <td>1.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.236593</td>\n",
       "      <td>4.012090</td>\n",
       "      <td>4.511461</td>\n",
       "      <td>3.343574</td>\n",
       "      <td>0.655896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Castifliango   Buckling     Static    Fatigue      Bonus\n",
       "count     27.000000  27.000000  27.000000  27.000000  27.000000\n",
       "mean      14.444444  14.592593  15.259259  16.777778   1.740741\n",
       "std        4.236593   4.012090   4.511461   3.343574   0.655896\n",
       "min        6.000000   5.000000   3.000000   6.000000   0.000000\n",
       "25%       12.000000  14.000000  14.000000  15.000000   2.000000\n",
       "50%       13.000000  14.000000  16.000000  17.000000   2.000000\n",
       "75%       18.500000  17.500000  19.000000  20.000000   2.000000\n",
       "max       20.000000  20.000000  20.000000  20.000000   2.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_points = final_points[['Castifliango', 'Buckling', 'Static', 'Fatigue', 'Bonus']]\n",
    "la_points.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jHwH+uGaer5LstfxzYI+scvei/cDHgVuBlcB/Am/sd5vr2n8+yQ4nz5D8sBxV\npve/lWUowWewD/A8yd74K4Gb0u9VqT6HDN6HXPZ6B15H8o/zGMla0+yM4y8nGYBvJtnhZvMMYjb6\nTs8Frkx/b64A5mQc/5fAven37ybgn7KMX/f83XS/N3WjZZgOnAvcAtwA7Jdx/DencVcC15D8Q9Fp\n/Mn6/bx2Pmef9MPMzKzPvAOXmZlZn3kwNjMz6zMPxmZmZn3mwdjMzKzPPBibmZn1mQdjMzOzPvNg\nbGZm1mcejM3MzPrMg7GZmVmfeTCuAEm3SvrdmukVktamlx/bT9LqyeY1s/KTdKekffrdDuucB+Oc\nSTpC0vWSfi3pgfQ6vB13mnSg/evaxyLidyLiJ+nz+5Kc5H77SC4/BjWX7qqd18z6S9IqSeslPZn+\nRjyZXuFnqtecK+l/1T4WEbtExE/zba3lyYNxjiQdD/wD8DckF5beEfga8K4c0y4GVkXEf+eYw8yy\nEcAhETErIl6a/l3T70ZZ73kwzomkWcDJwJ9ExCUR8XREPB8RP4yIEyW9QdJ/SlqXrjF/Jb0m5sTr\nvyxpXNITkn4u6TWSPgy8H/h0+h/0Jem890h6q6Sjga8De6fPL2/QrnskTVyarFkbNkj6iKS70s3e\nX615bpqkL0l6RNKvJH08nX9a+vx2ki6R9Fj6+j/K5502K70XXTJUiQslPZT2uxFJr06f+xjwXuCk\ntI9/J3189UT5SdJWkr6Z9utbJZ2QXnsYSZul/XTHmnwvWtOWdKiksfT1P5G0a/5vgXkwzs/ewJYk\n1+Vs5HngkySX2dqb5NqdfwIg6feAfYFXRcRs4D3AYxHxdeA84Ivpf9BLagNGxFnAR4Fr0udPbtLG\nSdtQ4xDg9SSXr3tP2jaAPwYOAnYjucj771OzORy4gORyZQuAdwOflzTcpD1mlrgMeCVJ/7kF+CZA\nRPwzSd/6fNrH/6DBaz+bvm5Hkj76AV7cNye9VJ+kNwD/Bzia5HfhLOCS2n/SLR8ejPOzDfBoRGxo\n9GRE3BQRP4vEfcDpJNeBBXgWeCnwGkmKiF9EDhegb9KGCX8bEb+OiNXACDCUPv5u4LSIeCgingBO\nmXiBpJeTDO4nRMSzEfFz4Awgs4vNm1XIxeka8FpJ30374zkRsT4ifgP8NfB6SVu1GO/dwN+k/fYB\nkutn11KD10z4MMn1j29K2/GN9PE3tLNA1j4Pxvl5DHjZxGbbepL+h6TL0k1RjwOfA14GEBEjJB3o\na8C4pH/OGeIEAAAgAElEQVSRNDPrBk7Vhhq1/wSsBybasT2wuua52vvbAWsjYn3NY/cCO2TTcrNK\nWRIR89LbYWkJ6Itp+edx4Jcka7P1fXMy2wH310yvnmzGBhYBJ9T8c7COZC3bfTdnHozzcw3wDMnm\n20b+GbgDeGVEzAH+gpr/WCPiqxGxJ/Aa4NXAn088lWEbp2xDEw8BC2umd6y5/yAwT9LWdc8/0EVb\nzaqqvs8dCRwMDKf98lXpPBPzNfsNWMMkfTMinif5XZpR83zt3turgZNr/jmYGxEzI+KilpfGOuLB\nOCcR8SSwHPiapCXpThXTJR0s6Qska5hPRsR6SbsAH5t4raQ9Je2V1mmeBv4bmNjcPQ7slFEzXzpZ\nG1rwbeBYSdtLmgN8euKJiLgf+E/gbyVtKWk34EPAuRm126zKXkoyYK5L/6H9PC8egJv9BnybZAev\n2ZIWsul+IGPA+9M18ENI9k+Z8HXg45L2BJA0U9I729hEbh3yYJyjiPgH4HjgL4GHSXZo+jjwPeDP\nSDrEkyQ7THyr5qWzSDrFWuAe4FHg79LnzgR2nagvTaRqp1k196dqQ6O4tdNfB64AbgZuBH4APFdT\nI38f8AqSteTvAH+Vbn43s40a9d0VJFueHiTZeevquufPAIbSIxW+3SDOcpLfm1XA5SQ7fD1T8/yx\nwGHAOuAPgEteaEzEdST/lP+zpLXAnSRHcFjOFNHZVk9JO5N8yEGy+WQnkh/cf8yueVYWkg4G/jki\nXtHvtlh3JJ0JvBMYj4jd0sfmkvT3RSQ/8u9Jd9yzgpP0CZK69Nv63RabXMdrxhFxV0TsHhF7kBz6\n8l8ka3w2ACS9RNLb0+MWdyD5b/y7zV5npbCC5JCYWicCV0bEq4GrgM/0vFXWkrR0tHd6vPJvA8fh\nvll4WW2mPhD4VXr4iw0GkZzUZC3JZurbSAZkK7mIuJpkE2atJcDZ6f2zmXzHROu/LUnKSE8CPwYu\nJDls0QosqwO53wv8a0axrAQi4mlgr363w3pm24lj3SNijaRt+90gaywi7gF+p9/tsPZ0vWYsaXPg\nUJL/vsxsMGR5iJ3ZwMtizfjtwI0R8UijJyW505oBEdHqMdxFNC5pfkSMK7mq0MONZnJ/N0u029+z\nqBm/jyabqCOiZ7fly5dXMpfz5Zsr/abmeCud2pNMAFwKLEvvL6XmcJh6Vf3O9Ps76nzZ5Uu/qTne\n2tfVYCxpBsnOW95Tz6wiJJ1PctKWnSXdJ+koknOPv03SL0iul33KVDHMrD1dbaaO5NzDv5VRWzKx\natWqSuZyvvLmKpuIOGKSpw7saUNa4O+o8xU1X7sqdwauoaGh5jOVMJfzlTeX5cffUecrar52dXwG\nrpYTSJF3DrNuSSLf2q6Icu/A1RL3dyuDIvb3yq0Zm5mZlU3lBuPR0dFK5nK+8uay/Pg76nxFzdeu\nyg3GZmZmZeOasRnFrCGVkfu7lUER+7vXjM3MzPqscoOxazrOV7Rclh9/R52vqPnaVbnB2MzMrGy6\nqhlLmg2cQXK5rg3A0RFxXd08riFZ4RWxhlRG7u9WBkXs791etek04IcR8W5J04EZXcYzMzMbOB1v\nppY0C3hLRKwAiIjnIuLJzFrWIdd0nK9ouSw//o46X1HztaubmvErgEclrZB0k6TTJW2VVcPMzMwG\nRcc1Y0mvB64F9o6IGySdCjwREcvr5oulS5eyePFiAObMmcPQ0BDDw8PAxv9WPO3pfk7vv//+JDWk\nZBqG07+dTk/cX5XeP9s1Y7OCKGLNuJvBeD5wTUTslE7vC5wQEe+qm8+d0wqviJ2zjNzfrQyK2N87\n3kwdEePAakk7pw8dANzeabysuKbjfEXLZfnxd9T5ipqvXd3uTX0McJ6kzYG7gaO6b5KZFZWk44AP\nkRzKeAtwVET8pr+tMis/n5vajGJutioaSdsDVwO7RMRvJF0A/CAizqmZx/3dCq+I/b3bNWMzGyyb\nAVtL2kByXoEH+9wes0qo3OkwXdNxvqLlqoqIeBD4EnAf8ADweERc2c82+TvqfEXN1y6vGZtZSyTN\nAZYAi4AngIskHRER59fOt2zZsp4dyjg2NpZpvKLl83Q+0xtNTA93OT1xfxWdcs3YjGLWkIpG0h8C\nB0XEh9PpDwJvjIhP1Mzj/m6FV8T+XrnN1GaWm/uAN0l6iZJfswOAO/rcJrNKqNxg7JqO8xUtV1VE\nxM+Ai4CVwM8BAaf3s03+jjpfUfO1yzVjM2tZRJwMnNzvdphVjWvGZhSzhlRG7u9WBkXs75XbTG1m\nZlY2XQ3GklZJ+rmklZJ+llWjuuGajvMVLZflx99R5ytqvnZ1WzPeAAxHxLosGmNmZjaIuqoZS7oH\n2DMiHptiHteQrPCKWEMqI/d3K4Mi9vdua8YB/Juk6yV9uMtYZmZmA6nbzdT7RMRDkn6LZFC+IyKu\nrp+pl6fHO/XUU3ONXztdW4Mocr7DDjucdevGycvcufNZu3ZNy+2ZbLqX72ejnImJ6eEupyfur8Ly\nMzo6+sJnW7V8VV62QcjXtojI5AYsB45v8Hj00sjISCVzdZMPCIgObiMtzpfNZ9zPz67z96jVGxEZ\n9bUi36rc33udr8rL1u98RezvHdeMJc0ApkXEU5K2Bq4ATo6IK+rmi05zWDZ6VB/JMX7+ilhDKiP3\nd8vCggWLGR+/N+csxerv3Wymng98T1Kkcc6rH4jNzMzalQzE+Q6WRdPxDlwRcU9EDEXE7hHx2og4\nJcuGdcrHAWaasbfZKvzZWT6q3AervGz9yNfr37N2+QxcZmZmfeZzUw8A14ybc804G+7vloVe9Mei\n9XevGZuZmfVZ5QZj13QyzdjbbBX+7CwfVe6DVV62fuRzzdjMzMym5JrxAHDNuDnXjFsjaTZwBvA7\nJBeKOToirqt53v3dujaINeNuT4dpZoPlNOCHEfFuSdOBGf1ukFkVVG4ztWs6mWbsbbYKf3ZVIGkW\n8JaIWAEQEc9FxJP9bFOV+2CVl60f+VwzNrOqeAXwqKQVkm6SdLqkrfrdKLMq6LpmLGkacANwf0Qc\n2uB515D6zDXj5lwzbk7S64Frgb0j4gZJpwJPRMTymnli6dKlPbtKm6erOb3//vuT9MdkuvurqNVP\nZx1/4v6q9P7Zbff3LAbj44DXA7M8GBeTB+PmPBg3J2k+cE1E7JRO7wucEBHvqpnH/d26Nog7cHW1\nmVrSQuAdJHtXFoJrOplm7G22Cn92VRAR48BqSTunDx0A3N7HJlW6D1Z52fqRr+g14273pv4y8OfA\n7AzaYmbFdwxwnqTNgbuBo/rcHrNK6OZ6xocAb4+IT0gaBj5Vu7mqZj7XkPo8vbH+AvnUYPZ/YTN1\nEZa3k+nsa1QT91el99uvIZWRN1NbFgZxM3U3g/HngQ8AzwFbAS8FvhsRR9bN587ZZ64ZN+eacTbc\n3y0LgzgYd3M945MiYsd0Z47DgavqB+J+cE0n04y9zVbhz87yUeU+WOVl60e+oteMfZyxmZlZn/nc\n1APAm6mb82bqbLi/Wxa8mdrMzMx6rnKDsWs6mWbsbbYKf3aWjyr3wSovWz/yuWZsZmZmU3LNeAC4\nZtyca8bZcH+3LLhmbGZm1sSCBYuRlNttEFVuMHZNJ9OMvc1W4c/O8lHlPljkZRsfv5dkzbKb28gU\nz+VhNKe42ajcYGxmZlY2rhkPANeMm3PNOBvu74OhCjXdovX3jq/aJGlL4CfAFmmciyLi5E7jmZmZ\nDapuzk39DLB/ROwODAFvl7RXZi3rkGs6mWbsbbYKf3aWjyr3wSovW5qx4vna01XNOCLWp3e3JFk7\n9vYpMzOzNnVVM5Y0DbgReCXwtYj4TIN5XEPqM9eMm3PNOBvu74PBNePm8XtWMwaIiA3A7pJmARdL\nek1E3F4/37Jly1i8eDEAc+bMYWhoqO8Xk291et68BaxbNz75m5ABaUuSrf69MJr+Hc5wevPcjw2c\nNm0GGzasbz5j10bTv8NdTk/cX5VJq4oi/Qf8BuD+iDi03+0xq4yIyOQG/BVwfIPHo5dGRkYyjQcE\nxCS3kSmea+c2VY4s8rUav9N8ncZvJ19WOSbLlXX8TdsfkU1f6+cNOA74JnDpJM9n2Puay7q/Fylf\nkZctm/7Sy/5en694/b3jmrGkl0mand7fCngbcGen8cys2CQtBN4BnNHvtphVTcc1Y0mvBc4m2Qls\nGnBBRHyuwXzRaY4iyL82AlWoj/g9ah4/Sl4zlnQh8DlgNvCpaLCZuuz93VrjmnHz+O32945rxhFx\nC7BHp683s/KQdAgwHhFjkoZJfs0aKvM+Ip5ubXqjienhjKfLFn/i/io61u527XZvlLyGxJS1halq\nHu3VF7qvsWQRv9N8rhm38h5F9L/m2+kN+DxwH3A38BDwFHBOg/ky7X/NFLmuWqZc7ebLpr/0sr/X\n5ytef/e5qc2sqYg4KSJ2jIidgMOBqyLiyH63y6wqfG7qJlwzLkL8XuQoXg2pqCTth2vGA8014+bx\n2+3vHoyb8GBchPi9yFG8zllGZe/v1hoPxs3jt9vfK7eZurfnV+1lLucrby7LS5XP31zlZUszVjxf\neyo3GJuZmZWNN1M34c3URYjfixzF22xVRmXv79Yab6ZuHn/gN1ObmZmVTeUGY9eMna94uSwvVa6r\nVnnZ0owVz9eebs5NvVDSVZJuk3SLpGOybJiZmdmg6Obc1AuABZGcHm8myXWNl0TEnXXzlbqG5Jpx\nEeL3IkfxakhlVPb+bq1xzbh5/J7VjCNiTUSMpfefAu4Adug0npmZ2aDKpGYsaTEwBFyXRbxuuGbs\nfMXLZXmpcl21ysuWZqx4vvZ0fNWmCekm6ouAY9M15E3keRWXefMWsG7deLeL0cQo2V81pH6aJs87\nfr7x8og/SldXcTGzgdHVccaSpgPfB34UEadNMk+uNaTy1y56kaPs8XuRo3g1pDJyzXgwlP93t3j9\nvdvN1GcBt082EJuZmVlz3RzatA/wfuCtklZKuknSwdk1rVOjFc3lfOXNZXmpcl21ysuWZqx4vvZ0\nXDOOiJ8Cm2XYFjMzs4FU+nNTl7920YscZY/fixzFqyGVkWvGg6H8v7vF6++VOx2mmZlZ2VRwMB6t\naC7nK2+uaijiKXCrXFet8rKlGSuerz1dH2dsZgPjOeD42lPgSrqi/hS4ZtY+14ybZ8g5fi9ylD1+\nL3IUr4ZUdJIuBr4SEf9e85hrxgOg/L+7xevvFdxMbWZ5K9IpcM2qoIKbqUfZeGrCKuVyvvLmqpZm\np8DN8/S39dOnnnpqrvHzyHfYYYfnegpfaUsinskt/ouNpn+HO5ieuN/o+SziT5Uv6/gT91c1yNGa\nCm6mHiXbH9mpNmdklavVTSad5ut0k0yr+bLa5DNVvqw3K9XnKt5mqyJqdgrcXm+mHh0dfWGgK0u+\n1jfxjlLM/t5Njlbz5dEfa/MVr79XcDDOPEPO8XuRo+zxe5GjeJ2ziCSdAzwaEcdP8rxrxk34N2sw\n4ve0ZizpTEnjkm7uJo6ZFV9xT4FrVn7d7sC1Ajgoi4ZkZ7SiuZyvvLmqISJ+GhGbRcRQROweEXtE\nxOX9bFO1j8XtZS7n67euBuOIuBpYl1FbzMzMBlLXNWNJi4DLImK3SZ6P97736K5yTOWCC86i3LWF\nXuQoe/xe5CheDamMXDNuzjXjwYjfbn/vyaFNF1xwB/CydGorYEdgl3R64uQ9nUxPlKpHyWbX90bT\necefmKbJ845frvgT91dhZtZURHR1AxYBN0/xfEDkdPtBbBp/JOMcU7U/q1ytvked5uv0M2g1X1af\n8VT5sv4e1efK83uaxO+2r5Xhli5nz4yMjJQuX/n7e1b9Zap8efTHkZzjv7j90Ua/iYhMzsCl9GZm\nZmYd6KpmLOl8ku1z2wDjwPKIWFE3T5DbtvkfAoeQX3xw/aUI8XuRo3g1pDJyzbg514wHI367/b2r\nmnFEHNHN682sei677LLcYm+33XbsueeeucU36xefm7o0uZyvvLkGywc+cHpusZ966nL++7/Xs/nm\nmwPlPB1mG9mobv8bhHztqeBgbGb99OST+a0ZT5u2Bd4MblXUk3NTu2bc7xxlj9+LHMWrIZVRvv09\nGYyffvoptthii9xy5M0148GI325/9/WMzczM+qyCg/FoRXM5X3lzWV58bmrnK26+9lRwMDYzMysX\n14ybcv2l//F7kaN4NaQycs24OdeMByO+a8ZmZmYl09VgLOlgSXdKukvSCVk1qjujFc3lfOXNVR1F\n6/OuGTtfcfO1p+PBWNI04KvAQcCuwPsk7TL1q3phrKK5nK+8uaqhiH1+bKy3n2Nv81W5/w1CvvZ0\ns2a8F/DLiLg3Ip4FvgUsyaZZ3Xi8ormcr7y5KqNwff7xx3v7OfY2X5X73yDka083g/EOwOqa6fvT\nx8ysmtznzXLSk9NhzpixfS5xn3/+v3nmmfpHV+WSq7Fe5nK+8uYaLHn1d4Bp02YybdrGdYhVq1bl\nlquR3ubrZS7n67eOD22S9Cbgf0fEwen0iSQXVP5C3Xw+kawZlP7Qplb6vPu7WaLd/t7NYLwZ8Avg\nAOAh4GfA+yLijo4Cmlmhuc+b5afjzdQR8bykTwBXkNSez3SnNKsu93mz/OR+Bi4zMzObWq5n4JI0\nW9KFku6QdJukN+aY6zhJt0q6WdJ5kjI9X56kMyWNS7q55rG5kq6Q9AtJP5Y0O+d8X0zfyzFJ35E0\nK898Nc99StIGSfPyzCXpT9Plu0XSKVnkmiyfpNdJukbSSkk/k7RnRrkWSroq/b7fIumY9PHcvitF\n4f6eeb5K9Pep8uXR53vZ39PY2fT5iMjtBnwDOCq9Px2YlVOe7YG7gS3S6QuAIzPOsS8wBNxc89gX\ngE+n908ATsk534HAtPT+KcDf5pkvfXwhcDlwDzAvx2UbJtn8OT2dflnO7+WPgd9L778dGMko1wJg\nKL0/k6TGukue35Wi3NzfM89Xif4+xfLl0ud72d/TeJn0+dzWjNP/4t4SESsAIuK5iHgyr3zAZsDW\nkqYDM4AHswweEVcD6+oeXgKcnd4/G/j9PPNFxJURsSGdvJak4+SWL/Vl4M+zyjNFro+RfFmfS+d5\nNOd8G4CJ/1TnAA9klGtNRIyl958C7iD5nHL7rhSB+3v2+arS36fIl0uf72V/T/Nl0ufz3Ez9CuBR\nSSsk3STpdElb5ZEoIh4EvgTcR/ImPx4RV+aRq862ETGetmENsG0Pck44GvhRngkkHQqsjohb8syT\n2hn4XUnXShrJcjPSJI4D/l7SfcAXgc9knUDSYpL/0K8F5vfxu9IL7u/5qlp/h972+dz7O3TX5/Mc\njKcDewBfi4g9gPXAiXkkkjSH5L+QRSSbsGZKOiKPXE30ZG84SX8BPBsR5+eYYyvgJGB57cN55SP5\nvsyNiDcBnwa+nWMuSP4rPzYidiTpqGdlGVzSTOCiNMdTbPrdqNqek+7vOalof4fe9vlc+zt03+fz\nHIzvJ/kv64Z0+iKSzpqHA4G7I2JtRDwPfBd4c065ao1Lmg8gaQHwcN4JJS0D3gHk/ePzSmAx8HNJ\n95BsdrlRUl5rA6tJPjci4npgg6RtcsoFsDQiLk7zXURy3uVMpJtOLwLOjYhL0od7/l3pMff3HFS4\nv0Nv+3xu/R2y6fO5Dcbp6vlqSTunDx0A3J5TuvuAN0l6iSSlufI4/lG8+L/FS4Fl6f2lwCX1L8gy\nn6SDSeo5h0bEJicCzTJfRNwaEQsiYqeIeAXJj+3uEZHVD1D9e3kx8FaA9DuzeUQ8llGuRvkekLRf\nmu8A4K4Mc50F3B4Rp9U8lvd3pa/c37PPV7H+/qJ8qTz7fC/7O2TR57Pao2ySvcxeB1xPcu2q7wKz\nc8y1nKRD3kxSLN884/jnk+wk8gzJj8FRwFzgSpK9564A5uSc75fAvcBN6e2f8sxX9/zdZLc3daNl\nmw6cC9wC3ADsl/N7+eY0z0rgGpIfnixy7QM8n37nV6af08HAvLy+K0W5ub9nnq8S/X2K5culz/ey\nv6f5MunzPumHmZlZn+V60g8zMzNrzoOxmZlZn3kwNjMz6zMPxmZmZn3mwdjMzKzPPBibmZn1mQdj\nMzOzPvNgbGZm1mcejM3MzPrMg7GZmVmfeTA2JB0h6fJ+t8PMbFB5MC4JSftK+qmkxyU9Kuk/JL1e\n0lJJ/9FGnEWSNkh64bOPiPMj4uB8Wm5mZs1M73cDrDlJLwUuAz4CXAhsAbyF5Kok0N5FzpXOn/eF\nw83MrEVeMy6HnYGIiG9H4pmIuBJ4DvgXYG9Jv5a0FkDSOyTdJOkJSfdKWl4T6/+mfx+X9KSkN9av\nXUvaVdIVkh6T9JCkE3u1oGZmg8iDcTncBTwv6RuSDpY0ByAi7gQ+ClwTES+NiHnp/E8BH4yI2cAh\nwEclHZo+97vp31kRMSsirkunA0DSTODfgB8C2wGvAv495+UzMxtoHoxLICJ+DewLbABOBx6RdLGk\nbSeZ/ycRcVt6/1bgW8B+dbNNtpn6ncBDEXFqRPwmIv4rIq7PZEHMzKwhD8YlERG/iIijI2JHYFdg\nB+DURvNK2kvSVZIelvQ4Sa35ZS2mejnwq0wabWZmLfFgXEIRcRfwDZJBudHOW+cDFwM7RMQc4P+w\ncU242c5eq4FXZtNSMzNrhQfjEpD0aknHS9ohnX458D7gGmAcWChp85qXzATWRcSzkvYCjqh57hGS\nzd2TDbjfBxZIOkbSFpJmpjHMzCwnHozL4dfAG4HrJP0a+E/gZuDPgKuA24A1kh5O5/848FlJTwB/\nCVwwESgingY+B/xU0tr6gTYingLeBhwKrCHZeWw4v0UzMzNFTL3VUtKZJDv1jEfEbuljc0l+4BcB\nq4D3RMQT+TbVzMysmlpZM14BHFT32InAlRHxapI1s89k3TAzM7NB0XTNGJJTKAKX1awZ3wnsFxHj\nkhYAoxGxS75NNTMzq6ZOa8bbRsQ4QESsARoe72pmZmbNZXVu6klXryW1c95ks8qKCJ8P3Mwa6nTN\neFzSfIB0M/XDU80cEZnfli9fXoqYbmt52ppYTvK/ZdY3M7PJtToYixefPvFSYFl6fylwSYZtMjMz\nGyhNB2NJ55Mc17qzpPskHQWcArxN0i+AA9Lpnlq1alUpYuYV123NK24eMc3Mpta0ZhwRR0zy1IEZ\nt6UtQ0NDpYiZV1y3Na+4+bTVzGwqLR3a1FUCKfLOYZYFSeRX3xXhHbjMbBI+HaaZmVmflXYwHh0d\nLUXMvOK6rXnFzSOmmdnUSjsYm5mZVYVrxmYp14zNrF+8ZmxmZtZnpR2MB7226ba6Zmxm1VHawdjM\nzKwqXDM2S7lmbGb94jVjMzOzPivtYDzotU231TVjM6uO0g7GZmZmVeGasVnKNWMz6xevGZuZmfVZ\naQfjQa9tuq2uGZtZdXQ1GEs6TtKtkm6WdJ6kLbJqmJmZ2aDouGYsaXvgamCXiPiNpAuAH0TEOXXz\nuWZspeCasZn1y/QuX78ZsLWkDcAM4MHum2RmZjZYOt5MHREPAl8C7gMeAB6PiCuzalgzg17bdFtd\nMzaz6uh4MJY0B1gCLAK2B2ZKOiKrhpmZmQ2KbjZTHwjcHRFrASR9F3gzcH79jMuWLWPx4sUAzJkz\nh6GhIYaHh4GNazdFmB4eHs4t/oQiLW+j6YnHyrD8eXxeaSuB4Zr7dDA9cX8VZmbNdLMD117AmcAb\ngGeAFcD1EfG1uvm8A5eVgnfgMrN+6aZm/DPgImAl8HNAwOkZtaupQa9tuq2uGZtZdXS1N3VEnAyc\nnFFbzMzMBpLPTW2W8mZqM+uX0p4O08zMrCpKOxgPem3TbXXN2Myqo7SDsZmZWVW4ZmyWcs3YzPrF\na8ZmZmZ9VtrBeNBrm26ra8ZmVh2lHYzNzMyqwjVjs5RrxmbWL14zNjMz67PSDsaDXtt0W10zNrPq\nKO1gbGZmVhWuGZulXDM2s37xmrGZmVmflXYwHvTaptvqmrGZVUdpB2MzM7Oq6KpmLGk2cAbwO8AG\n4OiIuK5uHteMrRRcMzazfpne5etPA34YEe+WNB2YkUGbzMzMBkrHm6klzQLeEhErACLiuYh4MrOW\nNTHotU231TVjM6uObmrGrwAelbRC0k2STpe0VVYNMzMzGxTdbKaeDuwBfDwibpB0KnAisLx+xmXL\nlrF48WIA5syZw9DQEMPDw8DGtZsiTA8PD+cWf0KRlrfR9MRjRV3+efMWsG7dOPkaBYZr7tPB9MT9\nVZm1ysyqq+MduCTNB66JiJ3S6X2BEyLiXXXzeQcuy0zeO1l5By4z64eON1NHxDiwWtLO6UMHALdn\n0qoWDHpt022FfOq7ecQ0M5tat3tTHwOcJ2lz4G7gqO6bZGZmNlh8bmorFW+mNrMq8hm4zMzM+qy0\ng3GZaptua/Yx08gliWlmNrXSDsZmZmZV4ZqxlYprxmZWRV4zNjMz67PSDsZlqm26rdnHTCOXJKaZ\n2dRKOxibmZlVhWvGViquGZtZFXnN2MzMrM9KOxiXqbbptmYfM41ckphmZlMr7WBsZmZWFa4ZW6m4\nZmxmVeQ1YzMzsz4r7WBcptqm25p9zDRySWKamU2ttIOxmZlZVbhmbKXimrGZVVHXa8aSpkm6SdKl\nWTTIzMxs0GSxmfpY4PYM4rSlTLVNtzX7mGnkksQ0M5taV4OxpIXAO4AzsmmOmZnZ4OmqZizpQuBz\nwGzgUxFxaIN5XDO2zLhmbGZVNL3TF0o6BBiPiDFJwyS/ZA0tW7aMxYsXAzBnzhyGhoYYHh4GNm7C\n9HR1pg877HDWrRsnP6PAcM19Cjg9cX8VZmbNdLxmLOnzwAeA54CtgJcC342II+vmy2XNeHR09IUB\noMgx84pb5LZuuvY6ysbBqlu1sbOMOxF7JOOYG2N7zdjMJtNxzTgiToqIHSNiJ+Bw4Kr6gdjMzMya\ny+Q4Y0n74Zqxpcpc13XN2Mz6wSf9sMx5MG4c24OxmU2mtKfDLNPxsIPe1vyO3c0jbh4xzcymVtrB\n2I8MAAUAAAj0SURBVMzMrCq8mdoy583UjWN7M7WZTcZrxmZmZn1W2sF40OuwZWqra8ZmZlMr7WBs\nZmZWFa4ZW+ZcM24c2zVjM5uM14zNzMz6rLSD8aDXYcvUVteMzcymVtrB2MzMrCpcM7bMuWbcOLZr\nxmY2Ga8Zm5mZ9VlpB+NBr8OWqa2uGZuZTa20g7GZmVlVuGZsmXPNuHFs14zNbDJeMzYzM+uzjgdj\nSQslXSXpNkm3SDomy4Y1M+h12DK11TVjM7OpTe/itc8Bx0fEmKSZwI2SroiIOzNqm5mZ2UDIrGYs\n6WLgKxHx73WPu2Y8YFwzbhzbNWMzm0wmNWNJi4Eh4Los4pmZmQ2SbjZTA5Buor4IODYinmo0z7Jl\ny1i8eDEAc+bMYWhoiOHhYWBjjbLd6YnHOn19o+n62K2+/rDDDmfduvFGi96V+fMX8a1vfaNh/vo2\nt7u8hx++jPHxezNv86ZGgTHgkzXTAMMdTk88NsyL67udxqufPpXk/8pu403cX4WZWTNdbaaWNB34\nPvCjiDhtknly2Uw9Ojr6wgDT75jNN8uO8uLBpOXITPbedbv8k7d5lM7a+qLodbGziNkodpZxJ2KP\nZBxzY2xvpjazyXQ7GJ8DPBoRx08xT+VrxvnVSCcfjLuO7Lpuz2N7MDazyXRzaNM+wPuBt0paKekm\nSQdn1zQzM7PB0PFgHBE/jYjNImIoInaPiD0i4vIsGzeVMh27m8exq2Vqq48zNjObms/AZWZm1mc+\nN3UGXDPeJLpjN4jtmrGZTcZrxmZmZn1W2sHYNePsY6aRSxIzr7h5xDQzm1ppB2MzM7OqcM04A64Z\nbxLdsRvEds3YzCbT9ekws/L000+zbt26XGJvueWWbLPNNrnENjMz61ZhBuN3vet9XH31f7DZZlu2\nNP/zzz/TxrxPMmvWPB55ZHU3TezCKJ2dYnHLdA22l0bJ/nSQecTMK24eMc3MplaYwXjNmkd55plL\ngH1bfMUorf5ozpz5P3jkkf9H802Qrcd8sbwGzGeYvM2jdDdoeIupmVlRlHgHruGSxMwrbh4x84qb\nR8y84uYR08xsaiUejM3MzKqhxIPxaEli5hU3j5h5xc0jZl5x84hpZja1Eg/GZmZm1VDiwXi4JDHz\niptHzLzi5hEzr7h5xDQzm1qJB2MzM7NqKPFgPFqSmHnFzSNmXnHziJlX3DximplNravBWNLBku6U\ndJekE7JqVGvGShIzr7hua7naamY2uY4HY0nTgK8CBwG7Au+TtEtWDWvu8ZLEzCuu21qutpqZTa6b\nNeO9gF9GxL0R8SzwLWBJNs0yMzMbHN2cDnMHoPZkz/eTDNAd2XLLzdl660+z2WatXdBh/fqVzJhx\nY0vzPv30gy22YlWL87Urj7h5xMwrbh4x84qbR0wzs6l1fAlFSX8AHBQRf5xOfwDYKyKOqZuv2tdP\nNGuRL6FoZpPpZs34AWDHmumF6WMv4h8gMzOzqXVTM74eeJWkRZK2AA4HLs2mWWZmZoOj4zXjiHhe\n0ieAK0gG9TMj4o7MWmZmZjYgOq4Zm5mZWTZyOwOXpIWSrpJ0m6RbJB3T/FUtx54m6SZJmW0WlzRb\n0oWS7kjb/MYMYh4n6VZJN0s6L92c30mcMyWNS7q55rG5kq6Q9AtJP5Y0O6O4X0zfgzFJ35E0q9uY\nNc99StIGSfOyaGv6+J+m7b1F0indxpT0OknXSFop6WeS9mwzZsPvfRafl5lVV56nw3wOOD4idgX2\nBj6e4UlBjgVuzyjWhNOAH0bEbwOvA7ra5C5pe+BPgT0iYjeSksDhHYZbQXJylVon/v/27idE6jKO\n4/j7QytkSa0eVEpMDbZDSOpJjNpKBSnaOndI6+YpOhhkh71GGdGhDhGKCQaxhHkRZLFL0EamlhGU\nsNFuhRv2h4hA0v10eJ6V3WEm3d/zzA4u3xc8MDO7fOZhfg/z/T3PPPMbYNT2fcAp4OVKuSeB+21v\nAi40yG2XiaQ1wE7gxwb9bJsr6RHgSWCj7Y3AgdJM4DVg2PZmYBh4fZ6ZncZ9jeMVQlikulaMbV+0\nfS7f/ptU3O4uzc1v6o8D75Vmzcq8A3jI9iEA21ds/1Uh+hbgdkl9wG3AjX7heQ7bnwJ/tDz8FHA4\n3z4MPF0j1/ao7el8d4y0S760rwBvAvvm28fr5O4FXrV9Jf/PpQqZ08DMrLWfNt8QuE5mu3G/hgrH\nK4SweC3ID0VIWgdsAj6vEDfzpl7zw+71wCVJh/Ly97uSlpYE2v4FeAOYIL2h/2l7tEJfZ6y0PZWf\n6yKwsmL2jOeBE6UhkoaASdvny7s0xwDwsKQxSZ/Md0m5gxeBA5ImSLPkxjPYWeN+DFi1AMcrhHCT\n6noxlrQMGAFeyDOFkqwngKk881BuNfQBW4C3bW8B/iEtKzYmqZ80G7oHuAtYJumZ0o7+j6o78SS9\nAvxr+2hhzlJgP2nJ99rDJZmz9AHLbW8FXgI+rJC5lzRW15IK88EmIW3GfevxiZ2TIYRrulqM8/Ls\nCHDE9scVIh8EhiSNAx8Aj0p6v0LuT6SZ2+l8f4RUnEvsAMZt/277KvARsK0wc7YpSasAJK0Gfq0V\nLGkP6aOAGicP9wLrgK8k/UBasv1SUo2Z4STpdcX2F8C0pBu7nmpnu20fy5kjNLjEa4dx37XjFUK4\n+XV7ZnwQ+Nb2WzXCbO+3vdb2BtJmqFO2n62QOwVMShrID22nfIPYBLBV0q2SlDNLNoW1rgQcB/bk\n27uBpic7c3Il7SJ9DDBk+3Jppu1vbK+2vcH2etKJz2bbTYpR62twDHgs93sAWGL7t8LMnyUN5szt\nwPcN+tlu3Nc6XiGExch2VxppFnuV9AOxZ4EzwK6K+YPA8Yp5D5CuKnaONNu6s0LmMKkAf03atLOk\nYc5R0uavy6Qi/xywHBgFviPtgO6vlHuBtOP5TG7vlGa2/H0cWFGpr33AEeA8cBoYrJC5LWedBT4j\nnTgUj3tgRenxihYt2uJtcdGPEEIIoccWZDd1CCGEEDqLYhxCCCH0WBTjEEIIoceiGIcQQgg9FsU4\nhBBC6LEoxiGEEEKPRTEOIYQQeuw/WR2tghtq2jUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0658c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = la_points.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The students did better on the static and fatigue failure problems. This makes sense, as we spent much more time on those topics. I really didn't do Castigliano's thereom justice but we spent some more time on buckling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overall Grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grades = pd.read_csv('smartsite-grades.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     84.398148\n",
       "std       6.698009\n",
       "min      64.200000\n",
       "25%      80.290000\n",
       "50%      84.370000\n",
       "75%      88.375000\n",
       "max      95.240000\n",
       "Name: Calculated Grade, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades['Calculated Grade'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JlFXiSVixeCGOA3Dqdz8VMV92LTqAsxYdwFGLDuCq/n1vnkPncuPe+wclfdApCwAAFwxe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a06043860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = grades['Calculated Grade'].hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grades are fairly well normally distributed except for the lowest grade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eaxWR+qGgF4mQjIwMCgsHkZ1dUPEOcjNnQqdOwcELFsApp4RbsIgknSbjiUTQ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\ntAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a064c14a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = probplot(grades['Calculated Grade'], dist=\"norm\", plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Piazza Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was curious if there was any correlation with the Piazza data to the course grade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "piazza = pd.read_csv('piazza.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "piazza = piazza[piazza['role'] != 'Instructor']\n",
    "piazza = piazza[piazza['days online'] >= 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "piazza['Last Name'] = [x.split(' ')[-1] for x in piazza['name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grades['Last Name'] = [x.split(', ')[0] for x in grades['Student Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_dat = pd.merge(grades, piazza, on='Last Name', how='outer')\n",
    "contributors = all_dat[all_dat['contributions'] != 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contributors['grade'] = contributors['Calculated Grade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7l5m/Bk6OiEOBbRFxIr/dn57rX0T8B2B3Zl4XEYNzvLTn+raHp2fm9+qFxnZE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\nStsj6WBgtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a127dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = contributors.plot(kind='scatter', x='contributions', y='Calculated Grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+H78BfLLpIrr0MOCWBdvfYgDDcaGIeCSdAZNfaLaSnsz/wx/kwSknAt+LiAvr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\npEIZ8pIkFer/A+10SrxqG6hVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a06126710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = contributors.plot(kind='scatter', x='questions', y='Calculated Grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a0a18da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = contributors.plot(kind='scatter', x='views', y='Calculated Grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/moorepants/miniconda3/lib/python3.5/site-packages/scipy/stats/stats.py:1285: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>grade</td>      <th>  R-squared:         </th> <td>   0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.9757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 15 Dec 2015</td> <th>  Prob (F-statistic):</th>  <td> 0.398</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:34:57</td>     <th>  Log-Likelihood:    </th> <td> -57.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    19</td>      <th>  AIC:               </th> <td>   121.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    16</td>      <th>  BIC:               </th> <td>   124.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   82.2398</td> <td>    3.129</td> <td>   26.281</td> <td> 0.000</td> <td>   75.606    88.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>views</th>     <td>    0.0841</td> <td>    0.063</td> <td>    1.342</td> <td> 0.198</td> <td>   -0.049     0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>questions</th> <td>   -0.1116</td> <td>    0.383</td> <td>   -0.291</td> <td> 0.775</td> <td>   -0.925     0.701</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.246</td> <th>  Durbin-Watson:     </th> <td>   2.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.325</td> <th>  Jarque-Bera (JB):  </th> <td>   1.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.005</td> <th>  Prob(JB):          </th> <td>   0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.840</td> <th>  Cond. No.          </th> <td>    141.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  grade   R-squared:                       0.109\n",
       "Model:                            OLS   Adj. R-squared:                 -0.003\n",
       "Method:                 Least Squares   F-statistic:                    0.9757\n",
       "Date:                Tue, 15 Dec 2015   Prob (F-statistic):              0.398\n",
       "Time:                        21:34:57   Log-Likelihood:                -57.586\n",
       "No. Observations:                  19   AIC:                             121.2\n",
       "Df Residuals:                      16   BIC:                             124.0\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     82.2398      3.129     26.281      0.000        75.606    88.874\n",
       "views          0.0841      0.063      1.342      0.198        -0.049     0.217\n",
       "questions     -0.1116      0.383     -0.291      0.775        -0.925     0.701\n",
       "==============================================================================\n",
       "Omnibus:                        2.246   Durbin-Watson:                   2.247\n",
       "Prob(Omnibus):                  0.325   Jarque-Bera (JB):                1.065\n",
       "Skew:                          -0.005   Prob(JB):                        0.587\n",
       "Kurtosis:                       1.840   Cond. No.                         141.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS.from_formula('grade ~ views + questions', contributors)\n",
    "res = model.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There doesn't seem to be any simple correlations between the Piazza stats and the course grades."
   ]
  }
 ],
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