{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conjoint Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Conjoint analysis is a technique that allows researchers to predict consumers' choice share. The analysis can be programmed using standard question types, such as the MaxDiff variation of the Matrix Table question. Instead of directly asking the survey respondents which attributes they find most relevant, conjoint analysis asks respondents to evaluate potential product profiles which include multiple product features \n",
    "\n",
    "There are several ways to show to respondents the product profiles. In Choice-Based Conjoint (CBC) respondents are shown multiple product conceptsn and asked which option they would choose. By varying the features shown to the respondents and observing their responses to the product profiles, one can statistically deduce the most desired product features and which attributes have the most impact on choice. \n",
    "\n",
    "The end result is a set of preference scores or *part-worth utilities* for each level of each attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MaxDiff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MaxDiff is a technique for obtaining preference or importance scores for multiple items. Respondents are shown a subset of the possible items in the exercise and asked to indicate the best and worst items. Item scores are typically estimated for each individual via hierarchical Bayes (HB) methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0wAAAEuCAYAAAC9PpLPAAAYImlDQ1BJQ0MgUHJvZmlsZQAAWIWV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\nAhBoXAL/P67HKoek5rTtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='MaxDiff.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conjoint Code I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "import pandas as pd  \n",
    "import numpy as np \n",
    "import statsmodels.api as sm \n",
    "import statsmodels.formula.api as smf  \n",
    "from patsy.contrasts import Sum\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The design of the survey is complex for several reasons. For example, in our simple case the attributes are:\n",
    "\n",
    "The attributes of the product are:\n",
    "    - 3 photography features which the user can use (editing, filtering and collage\\footnote{Artwork which is made from an assemblage of different forms.})\n",
    "    - 3 UI (mobile, Desktop or VR)\n",
    "    - 3 content sources (sharing quality content on the user's social media profiles is an efficient way to engage with followers). Examples are:\n",
    "        - BuzzSumo which enables users to search for relevant content using popular keywords and provides content based on social shares;\n",
    "        - Reddit which is a social networking website spanning all kinds of topics and provides the most engaging content, pictures, videos and other online occurrences;\n",
    "        - Pocket, a content finder app that can be used for searching online articles and saving them to read later.\n",
    "    - 3 shares (e.g. the best social media apps offer solutions for the user to share information across several social networks)\n",
    "    - 3 so-called differentiations(a kind of special sauce which differentiates the app from it competitors)\n",
    "\n",
    "The total number of combinations is \n",
    "\n",
    "\\begin{eqnarray}\n",
    "{\\rm{total}} = 3^5\\times 2 = 486,\n",
    "\\end{eqnarray}\n",
    "\n",
    "which is far too many for a consumer to rank (see picture below for a typical choice task)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEAOAA4AAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEP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\nACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgD/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='conjoint_terms.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now create 3 Pandas DataFrames containing the data from our files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myConjointDataR = pd.read_csv('conjoint-r.csv')\n",
    "myConjointDataLevelNames = pd.read_csv('conjoint-r-level-names.csv')\n",
    "myConjointDataProfileMatrix = pd.read_csv('conjoint-r-profiles-matrix.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to find out from users which of the attributes above they were most interested in. Each attribute has multiple layers. The photography e.g. feature has three layers:\n",
    "\n",
    "    - Editing\n",
    "    - Filtering\n",
    "    - Collage\n",
    "\n",
    "This is what a user could potentially do with the photography functionality on the social media platform. They can do one of these three pieces of functionality with photography. The levels of all attributes are contained in the following Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>edit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>collage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>content_source_one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>content_source_two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>content_type_one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>content_type_two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>content_type_three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ux1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ux2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ux3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>share1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>share2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>share3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dif1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dif2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dif3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                levels\n",
       "0                 edit\n",
       "1               filter\n",
       "2              collage\n",
       "3   content_source_one\n",
       "4   content_source_two\n",
       "5     content_type_one\n",
       "6     content_type_two\n",
       "7   content_type_three\n",
       "8                  ux1\n",
       "9                  ux2\n",
       "10                 ux3\n",
       "11              share1\n",
       "12              share2\n",
       "13              share3\n",
       "14                dif1\n",
       "15                dif2\n",
       "16                dif3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myConjointDataLevelNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next table shows our 500 observations (500 users) and six profiles (or combinations of attributes). We see that the different combinations or `profiles` that we tested for, were just six in all. "
   ]
  },
  {
   "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>profile1</th>\n",
       "      <th>profile2</th>\n",
       "      <th>profile3</th>\n",
       "      <th>profile4</th>\n",
       "      <th>profile5</th>\n",
       "      <th>profile6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     profile1  profile2  profile3  profile4  profile5  profile6\n",
       "0           2         1         4         2         3         3\n",
       "1           4         3         5         1         1         3\n",
       "2           3         4         1         2         3         2\n",
       "3           1         2         3         1         3         2\n",
       "4           2         3         2         3         3         2\n",
       "5           4         4         4         2         2         3\n",
       "6           3         2         3         4         2         3\n",
       "7           5         1         4         2         3         2\n",
       "8           2         3         4         4         2         2\n",
       "9           1         1         2         3         2         1\n",
       "10          4         3         5         3         2         1\n",
       "11          5         4         4         2         1         1\n",
       "12          1         3         3         4         3         3\n",
       "13          1         3         2         2         2         3\n",
       "14          4         4         4         4         3         3\n",
       "15          1         1         4         3         2         1\n",
       "16          3         2         3         3         1         2\n",
       "17          3         2         3         3         3         3\n",
       "18          2         4         1         4         2         3\n",
       "19          5         1         3         3         1         2\n",
       "20          4         1         5         1         2         1\n",
       "21          3         2         1         4         2         3\n",
       "22          4         1         5         2         2         1\n",
       "23          4         4         2         1         3         2\n",
       "24          1         1         3         2         2         1\n",
       "25          3         2         3         1         3         2\n",
       "26          2         1         5         2         2         2\n",
       "27          1         4         2         1         1         3\n",
       "28          3         4         3         1         2         1\n",
       "29          1         2         4         4         1         1\n",
       "..        ...       ...       ...       ...       ...       ...\n",
       "470         2         3         2         2         1         2\n",
       "471         5         4         2         4         3         3\n",
       "472         1         2         5         2         3         1\n",
       "473         5         4         1         2         1         1\n",
       "474         1         4         3         2         3         3\n",
       "475         4         1         4         1         1         3\n",
       "476         1         2         5         1         2         1\n",
       "477         1         3         4         3         3         1\n",
       "478         5         3         1         3         3         2\n",
       "479         2         1         3         3         3         3\n",
       "480         1         2         4         1         3         3\n",
       "481         3         2         4         4         3         1\n",
       "482         2         3         2         2         1         2\n",
       "483         5         2         2         4         2         1\n",
       "484         1         4         4         4         3         2\n",
       "485         4         3         1         3         2         3\n",
       "486         3         4         2         2         1         2\n",
       "487         4         4         1         4         3         3\n",
       "488         5         1         3         4         3         3\n",
       "489         2         3         3         4         1         3\n",
       "490         1         4         1         2         3         2\n",
       "491         5         2         4         1         3         3\n",
       "492         2         3         2         4         2         1\n",
       "493         3         3         1         4         1         1\n",
       "494         5         1         4         4         2         2\n",
       "495         2         2         1         2         3         1\n",
       "496         2         4         2         1         1         1\n",
       "497         2         4         4         1         3         1\n",
       "498         4         1         1         3         3         2\n",
       "499         4         1         4         3         1         2\n",
       "\n",
       "[500 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myConjointDataR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following DataFrame, the full dataset was aggregated and instead of 486 potential combinations we ended up with only 11. The reason there are 11 rows in this table in comparison with the table \n",
    "\n",
    "    myConjointDataProfilesMatrix\n",
    "    \n",
    "in our `R` code is because of hot-encoding. E.g. though there are three photos levels, only $(P1,P2)=(1,0),(0,1)$ are considered to avoid multicolinearity. To understand why there are twice as many lines we notice that the first two differ by the values of $U1$ and $U2$ which are interchanged. For example suppose `Desktop` (which corresponds to $U=2$) was the chosen UI. Instead of setting $U=2$, hot-encoding creates the pairs of values $(U1,U2)=(0,1)$ where $U1=0$ means \"not U1\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Rank</th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>U1</th>\n",
       "      <th>U2</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank  P1  P2  U1  U2  D1  D2  D3\n",
       "0      7   1   0   1   0   1   0   0\n",
       "1      6   1   0   0   1   1   0   0\n",
       "2      8   0   1   1   0   1   0   0\n",
       "3      1   0   1   0   1   1   0   0\n",
       "4     10   1   0   1   0   0   1   0\n",
       "5      3   1   0   0   1   0   1   0\n",
       "6      4   0   1   1   0   0   1   0\n",
       "7      2   0   1   0   1   0   1   0\n",
       "8      9   1   0   1   0   0   0   1\n",
       "9     12   1   0   0   1   0   0   1\n",
       "10    11   0   1   1   0   0   0   1\n",
       "11     5   0   1   0   1   0   0   1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myConjointData = pd.read_csv('conjoint-py.csv')\n",
    "myConjointData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We rename the columns for clarity. The first lines e.g. says a profile with features `PhotoF1`, `Ux1` and `SpecialSauce1` is ranked in 7th. We then use a hash table or Python dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Rank</th>\n",
       "      <th>PhotoF1</th>\n",
       "      <th>PhotoF2</th>\n",
       "      <th>Ux1</th>\n",
       "      <th>Ux2</th>\n",
       "      <th>SpecialSauce1</th>\n",
       "      <th>SpecialSauce2</th>\n",
       "      <th>SpecialSauce3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank  PhotoF1  PhotoF2  Ux1  Ux2  SpecialSauce1  SpecialSauce2  \\\n",
       "0      7        1        0    1    0              1              0   \n",
       "1      6        1        0    0    1              1              0   \n",
       "2      8        0        1    1    0              1              0   \n",
       "3      1        0        1    0    1              1              0   \n",
       "4     10        1        0    1    0              0              1   \n",
       "5      3        1        0    0    1              0              1   \n",
       "6      4        0        1    1    0              0              1   \n",
       "7      2        0        1    0    1              0              1   \n",
       "8      9        1        0    1    0              0              0   \n",
       "9     12        1        0    0    1              0              0   \n",
       "10    11        0        1    1    0              0              0   \n",
       "11     5        0        1    0    1              0              0   \n",
       "\n",
       "    SpecialSauce3  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "5               0  \n",
       "6               0  \n",
       "7               0  \n",
       "8               1  \n",
       "9               1  \n",
       "10              1  \n",
       "11              1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = {\"Rank\":\"Rank\", \"P1\": \"PhotoF1\",\"P2\": \"PhotoF2\", \"U1\": \"Ux1\", \\\n",
    "         \"U2\": \"Ux2\", \"D1\":\"SpecialSauce1\", \\\n",
    "         \"D2\":\"SpecialSauce2\", \"D3\":\"SpecialSauce3\"\\\n",
    "        }\n",
    "\n",
    "myConjointData.rename(columns = names, inplace=True)\n",
    "\n",
    "myConjointData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of these columns, with the exception of `Rank`, corresponds to a variable $X_i$ with $i=1,2,...,7$.\n",
    "The dependent variable is $y$ = `myContjointData.rank` and we are going to generate a linear multiple regression model.\n",
    "So I'm going to first assign a variable, call it `myLinearRegressionForConjoint` and then, again, we're going to call this `sm` method from one of our packages above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = myConjointData[['PhotoF1', 'PhotoF2', 'Ux1', 'Ux2', 'SpecialSauce1', \\\n",
    "                    'SpecialSauce2', 'SpecialSauce3']]              \n",
    "# Assign a constant\n",
    "X = sm.add_constant(X)\n",
    "# Assign our resulting test data to the y\n",
    "Y = myConjointData.Rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a linear regression we find that `SpecialSauce3` is the most important input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Rank</td>       <th>  R-squared:         </th> <td>   0.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Jan 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0471</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:56:14</td>     <th>  Log-Likelihood:    </th> <td> -24.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   59.04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     7</td>      <th>  BIC:               </th> <td>   61.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</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>const</th>         <td>    2.7857</td> <td>    0.302</td> <td>    9.211</td> <td> 0.000</td> <td>    2.071     3.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PhotoF1</th>       <td>    2.7262</td> <td>    0.722</td> <td>    3.777</td> <td> 0.007</td> <td>    1.020     4.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PhotoF2</th>       <td>    0.0595</td> <td>    0.722</td> <td>    0.082</td> <td> 0.937</td> <td>   -1.647     1.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ux1</th>           <td>    3.0595</td> <td>    0.722</td> <td>    4.239</td> <td> 0.004</td> <td>    1.353     4.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ux2</th>           <td>   -0.2738</td> <td>    0.722</td> <td>   -0.379</td> <td> 0.716</td> <td>   -1.980     1.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SpecialSauce1</th> <td>   -0.0714</td> <td>    1.003</td> <td>   -0.071</td> <td> 0.945</td> <td>   -2.443     2.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SpecialSauce2</th> <td>   -0.8214</td> <td>    1.003</td> <td>   -0.819</td> <td> 0.440</td> <td>   -3.193     1.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SpecialSauce3</th> <td>    3.6786</td> <td>    1.003</td> <td>    3.667</td> <td> 0.008</td> <td>    1.307     6.051</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.944</td> <th>  Durbin-Watson:     </th> <td>   2.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.624</td> <th>  Jarque-Bera (JB):  </th> <td>   0.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.092</td> <th>  Prob(JB):          </th> <td>   0.719</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.866</td> <th>  Cond. No.          </th> <td>2.37e+16</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   Rank   R-squared:                       0.707\n",
       "Model:                            OLS   Adj. R-squared:                  0.540\n",
       "Method:                 Least Squares   F-statistic:                     4.232\n",
       "Date:                Fri, 12 Jan 2018   Prob (F-statistic):             0.0471\n",
       "Time:                        12:56:14   Log-Likelihood:                -24.520\n",
       "No. Observations:                  12   AIC:                             59.04\n",
       "Df Residuals:                       7   BIC:                             61.46\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------\n",
       "const             2.7857      0.302      9.211      0.000         2.071     3.501\n",
       "PhotoF1           2.7262      0.722      3.777      0.007         1.020     4.433\n",
       "PhotoF2           0.0595      0.722      0.082      0.937        -1.647     1.766\n",
       "Ux1               3.0595      0.722      4.239      0.004         1.353     4.766\n",
       "Ux2              -0.2738      0.722     -0.379      0.716        -1.980     1.433\n",
       "SpecialSauce1    -0.0714      1.003     -0.071      0.945        -2.443     2.301\n",
       "SpecialSauce2    -0.8214      1.003     -0.819      0.440        -3.193     1.551\n",
       "SpecialSauce3     3.6786      1.003      3.667      0.008         1.307     6.051\n",
       "==============================================================================\n",
       "Omnibus:                        0.944   Durbin-Watson:                   2.685\n",
       "Prob(Omnibus):                  0.624   Jarque-Bera (JB):                0.660\n",
       "Skew:                           0.092   Prob(JB):                        0.719\n",
       "Kurtosis:                       1.866   Cond. No.                     2.37e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 4.96e-32. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a linear regression model \n",
    "myLRforConjoint = sm.OLS(Y,X).fit()\n",
    "myLRforConjoint.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part worth values & relative importance of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Part-worths**: they are scaled to an (arbitrary) additive constant within each attribute. The arbitrary origin on the scaling within each of the attribute depends on the dummy coding. When we use \"effects coding\", utilities are scaled to sum to 0 within each attribute. A plausible set of part-worth utilities for miles per gallon might look like:\n",
    "\n",
    "    30 MPG   -1.0\n",
    "    40 MPG    0.0\n",
    "    50 MPG    1.0\n",
    "    \n",
    "Other kinds of dummy coding arbitrarily set the part-worth of one level within each attribute to zero and estimate the remaining levels as contrasts with respect to zero. Again, the same cautions regarding interpretation apply. Whether we multiply all the part-worth utilities by a positive constant or add a constant to each level within a study, the interpretation is the same. Suppose we have two attributes with the following utilities:\n",
    "\n",
    " \n",
    "\n",
    "    Blue   30     Brand A  20\n",
    "\n",
    "    Red    20     Brand B  40\n",
    "\n",
    "    Green  10     Brand C  10\n",
    "\n",
    " \n",
    "\n",
    "The increase in preference from Green to Blue (20 points) is equal to the increase in preference between Brand A and Brand B (also 20 points). However, due to the arbitrary origin within each attribute, we cannot directly compare values between attributes to say that Red (20 utiles) is preferred equally to Brand A (20 utiles). And even though we are comparing utilities within the same attribute, we cannot say that Blue is three times as preferred as Green (30/10). Interval data do not support ratio operations.\n",
    "\n",
    "**Counts:** When using Choice-Based Conjoint (CBC), the researcher can analyze the data by counting the number of times an attribute level was chosen relative to the number of times it was available for choice. Counts proportions are closely related to conjoint utilities. Consider the following counts proportions:\n",
    "\n",
    "\n",
    "    Blue    30/(30+20+10) = 0.50     Brand A  0.40\n",
    "\n",
    "    Red     20/(30+20+10) = 0.30     Brand B  0.50\n",
    "\n",
    "    Green   10/(30+20+10) = 0.20     Brand C  0.10\n",
    "\n",
    " \n",
    "\n",
    "We can say that Brand A was chosen 4 times as often as Brand C (.40/.10). As with conjoint utilities, we cannot report that Brand A is preferred to Red.\n",
    "\n",
    "**Importance of each attribute**: to characterize the relative importance of each attribute we consider how much difference each attribute could make in the total utility of a product. That difference is the range in the attribute's utility values. We percentage those ranges, obtaining a set of attribute importance values that add to 100, as follows:\n",
    "\n",
    "                                   Percent\n",
    "\n",
    "                                Range     Importance\n",
    "\n",
    "      Brand (B - C)          60 - 20 = 40    26.7\n",
    "\n",
    "      Color (Red - Pink)     20 -  0 = 20    13.3\n",
    "\n",
    "      Price ($50 - $100)     90 -  0 = 90    60.0\n",
    "\n",
    "                              ----    ----\n",
    "\n",
    "                              150     100.0\n",
    "\n",
    " \n",
    "\n",
    "For this respondent, the importance of Brand is 26.7%, the importance of Color is 13.3%, and the importance of Price is 60%. Importances depend on the particular attribute levels chosen for the study. For example, with a narrower range of prices, Price would have been less important.\n",
    "\n",
    "**Important**: When calculating importances from CBC data, we suggest using utilities resulting from Latent Class (with multiple segments) or HB estimation, if there are attributes on which respondents disagree about preference order.\n",
    "\n",
    "Summarizing:\n",
    "\\begin{eqnarray}\n",
    "\\begin{array}{l}\n",
    "{\\rm{attribute\\,\\, }}{\\rm{ importance }} = \\,\\,\\max ({\\beta _i}) - \\min ({\\beta _i})\\\\\n",
    "{\\rm{relative\\,\\, attribute\\,\\, }}{\\rm{ importance }} = \\frac{{\\max ({\\beta _i}) - \\min ({\\beta _i})}}{{\\sum\\limits_j {\\max ({\\beta _j}) - \\min ({\\beta _j})} }}\n",
    "\\end{array}\n",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Reminders:** Linear Regression and plotting using `statsmodel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1126e0a10>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAGHCAYAAABmuoLpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npC7+B1p1aY5WdfGbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d49510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "data = sm.datasets.statecrime.load_pandas().data\n",
    "murder = data['murder']\n",
    "X = data[['poverty', 'hs_grad']]\n",
    "X[\"constant\"] = 1\n",
    "y = murder\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "fig, ax = plt.subplots()\n",
    "fig = sm.graphics.plot_fit(results,0, ax=ax)\n",
    "ax.set_ylabel(\"Y\")\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_title(\"Linear Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature is:  P2\n",
      "feature is:  P1\n",
      "feature is:  D2\n",
      "feature is:  D1\n",
      "feature is:  U1\n",
      "feature is:  D3\n",
      "feature is:  Rank\n",
      "rangePerFeature is:  [[0.059523809523807647, 2.7261904761904772], [-0.82142857142857184, -0.071428571428573673], [3.0595238095238098], [3.6785714285714302]]\n",
      "feature is:  U2\n",
      "rangePerFeature is:  [[0.059523809523807647, 2.7261904761904772], [-0.82142857142857184, -0.071428571428573673], [3.0595238095238098], [3.6785714285714302], [3.6785714285714302]]\n",
      "item is: [-0.82142857142857184, -0.071428571428573673]\n",
      "item is: [3.0595238095238098]\n",
      "item is: [3.6785714285714302]\n",
      "item is: [3.6785714285714302]\n",
      "relative_importance is: 100.0\n",
      "relative_importance is: 0.0\n",
      "relative_importance is: 0.0\n",
      "relative_importance is: 0.0\n"
     ]
    }
   ],
   "source": [
    "importance = []\n",
    "relative_importance = []\n",
    "rangePerFeature = []\n",
    "pwr = []\n",
    "\n",
    "begin = \"P\"   #first letter of the first feature (which is a string)\n",
    "tempRange = []\n",
    "for feature in names.keys():\n",
    "    print (\"feature is: \", feature)\n",
    "    if feature[0] == begin:\n",
    "        tempRange.append(myLRforConjoint.params[names[feature]])\n",
    "    elif feature == \"Rank\":\n",
    "        rangePerFeature.append(tempRange)\n",
    "        print (\"rangePerFeature is: \", rangePerFeature)\n",
    "    else:\n",
    "        rangePerFeature.append(tempRange)\n",
    "        begin = feature[0]\n",
    "        tempRange = [myLRforConjoint.params[names[feature]]]\n",
    "print(\"rangePerFeature is: \", rangePerFeature)\n",
    "\n",
    "\n",
    "for item in rangePerFeature[1:]:\n",
    "    importance.append(max(item) - min(item))\n",
    "    print (\"item is:\",item)\n",
    "for item in importance:\n",
    "    relative_importance.append(100* round(item/sum(importance),3))\n",
    "    print (\"relative_importance is:\",100* round(item/sum(importance),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "partworths = []\n",
    "\n",
    "item_levels = [1,2,3,4,5,6,7]\n",
    "\n",
    "for i in range(1,7):\n",
    "    part_worth_range = myLRforConjoint.params[item_levels[i-1]:item_levels[i]]\n",
    "    pwr.append(part_worth_range[0])\n",
    "    print(\"part_worth_range is:\", part_worth_range)\n",
    "    print(\"pwr:\", pwr)\n",
    "\n",
    "        \n",
    "print(\"importance is:\", importance)\n",
    "print(\"relative_importance is:\", relative_importance)\n",
    "print(\"rangePerFeature is:\", rangePerFeature)\n",
    "print(\"pwr is:\", pwr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.index('PhotoF1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpecialSauce2   -0.821429\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(part_worth_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 PhotoF1\n",
      "1 PhotoF2\n",
      "2 Ux1\n",
      "3 Ux2\n",
      "4 SpecialSauce1\n",
      "5 SpecialSauce2\n",
      "{'Ux2': -0.2738095238095255, 'SpecialSauce2': -0.82142857142857184, 'SpecialSauce1': -0.071428571428573673, 'Ux1': 3.0595238095238098, 'PhotoF1': 2.7261904761904772, 'PhotoF2': 0.059523809523807647}\n"
     ]
    }
   ],
   "source": [
    "dict = {}\n",
    "features = ['PhotoF1', 'PhotoF2', 'Ux1', 'Ux2', 'SpecialSauce1','SpecialSauce2', 'SpecialSauce3'] \n",
    "for idx,item in enumerate(features[0:-1]):\n",
    "    print(idx,item)\n",
    "    dict[item] = pwr[idx]\n",
    "print(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.67857142857\n",
      "3.05952380952\n",
      "2.72619047619\n"
     ]
    }
   ],
   "source": [
    "print(rangePerFeature[4][0])\n",
    "print(rangePerFeature[4][0])\n",
    "print(rangePerFeature[3][0])\n",
    "print(rangePerFeature[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'D1': 'SpecialSauce1',\n",
       " 'D2': 'SpecialSauce2',\n",
       " 'D3': 'SpecialSauce3',\n",
       " 'P1': 'PhotoF1',\n",
       " 'P2': 'PhotoF2',\n",
       " 'Rank': 'Rank',\n",
       " 'U1': 'Ux1',\n",
       " 'U2': 'Ux2'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.388771186440678, 0.323093220338983, 0.288135593220339]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize values for each feature \n",
    "raw = [3.67,3.05,2.72]\n",
    "norm = [float(i)/sum(raw) for i in raw]\n",
    "norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0134992223278581,\n",
       " 1.0115396799001157,\n",
       " -1.1052079951930152,\n",
       " 1.0190809061436532)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAFeCAYAAADXDSggAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2hzHcRzHcdYALtocx3Ecx3HWAP8PPuJWQSR5DXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11196f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph our winning product features\n",
    "\n",
    "labels = 'Special Sauce Feature Three', 'User Experience Feature One', 'Photo Feature One'\n",
    "sizes = [39, 32, 29]\n",
    "colors = ['yellowgreen', 'mediumpurple', 'lightskyblue'] \n",
    "explode = (0, 0.1, 0)    \n",
    "plt.pie(sizes,              \n",
    "        explode=explode,   \n",
    "        labels=labels,      \n",
    "        colors=colors,      \n",
    "        autopct='%1.1f%%',  \n",
    "        shadow=True,        \n",
    "        startangle=70       \n",
    "        )\n",
    "\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conjoint Code II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "import pandas as pd  \n",
    "import numpy as np \n",
    "import statsmodels.api as sm \n",
    "import statsmodels.formula.api as smf  \n",
    "from patsy.contrasts import Sum\n",
    "\n",
    "conjoint_data_frame = pd.read_csv('mobile_services_ranking.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Problem: Assessing mobile phone services"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Attributes:\n",
    "\n",
    "    - The mobile provider or brand\n",
    "    - Startup and monthly costs\n",
    "    - If the provider offered 4G services in the area\n",
    "    - Whether the provider had a retail location nearby\n",
    "    - Whether the provider supported Apple, Samsung, or Nexus phones in addition to tablet computers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Product profiles \n",
    "\n",
    "Product profiles are combinations of the attributes and are shown below. There are a total of 16 product profiles which are presented in random order to the consumer."
   ]
  },
  {
   "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>brand</th>\n",
       "      <th>startup</th>\n",
       "      <th>monthly</th>\n",
       "      <th>service</th>\n",
       "      <th>retail</th>\n",
       "      <th>apple</th>\n",
       "      <th>samsung</th>\n",
       "      <th>google</th>\n",
       "      <th>ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"AT&amp;T\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"Verizon\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"US Cellular\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"Verizon\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"Verizon\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\"Verizon\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>\"US Cellular\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\"AT&amp;T\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\"AT&amp;T\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"T-Mobile\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>\"US Cellular\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>\"T-Mobile\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple YES\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>\"T-Mobile\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>\"US Cellular\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>\"T-Mobile\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"$400\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>\"AT&amp;T\"</td>\n",
       "      <td>\"$300\"</td>\n",
       "      <td>\"$200\"</td>\n",
       "      <td>\"4G YES\"</td>\n",
       "      <td>\"Retail YES\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung YES\"</td>\n",
       "      <td>\"Nexus YES\"</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            brand startup monthly   service        retail        apple  \\\n",
       "0          \"AT&T\"  \"$100\"  \"$100\"   \"4G NO\"   \"Retail NO\"   \"Apple NO\"   \n",
       "1       \"Verizon\"  \"$300\"  \"$100\"   \"4G NO\"  \"Retail YES\"  \"Apple YES\"   \n",
       "2   \"US Cellular\"  \"$400\"  \"$200\"   \"4G NO\"   \"Retail NO\"   \"Apple NO\"   \n",
       "3       \"Verizon\"  \"$400\"  \"$400\"  \"4G YES\"  \"Retail YES\"   \"Apple NO\"   \n",
       "4       \"Verizon\"  \"$200\"  \"$300\"   \"4G NO\"   \"Retail NO\"   \"Apple NO\"   \n",
       "5       \"Verizon\"  \"$100\"  \"$200\"  \"4G YES\"   \"Retail NO\"  \"Apple YES\"   \n",
       "6   \"US Cellular\"  \"$300\"  \"$300\"  \"4G YES\"   \"Retail NO\"  \"Apple YES\"   \n",
       "7          \"AT&T\"  \"$400\"  \"$300\"   \"4G NO\"  \"Retail YES\"  \"Apple YES\"   \n",
       "8          \"AT&T\"  \"$200\"  \"$400\"  \"4G YES\"   \"Retail NO\"  \"Apple YES\"   \n",
       "9      \"T-Mobile\"  \"$400\"  \"$100\"  \"4G YES\"   \"Retail NO\"  \"Apple YES\"   \n",
       "10  \"US Cellular\"  \"$100\"  \"$400\"   \"4G NO\"  \"Retail YES\"  \"Apple YES\"   \n",
       "11     \"T-Mobile\"  \"$200\"  \"$200\"   \"4G NO\"  \"Retail YES\"  \"Apple YES\"   \n",
       "12     \"T-Mobile\"  \"$100\"  \"$300\"  \"4G YES\"  \"Retail YES\"   \"Apple NO\"   \n",
       "13  \"US Cellular\"  \"$200\"  \"$100\"  \"4G YES\"  \"Retail YES\"   \"Apple NO\"   \n",
       "14     \"T-Mobile\"  \"$300\"  \"$400\"   \"4G NO\"   \"Retail NO\"   \"Apple NO\"   \n",
       "15         \"AT&T\"  \"$300\"  \"$200\"  \"4G YES\"  \"Retail YES\"   \"Apple NO\"   \n",
       "\n",
       "          samsung       google  ranking  \n",
       "0    \"Samsung NO\"   \"Nexus NO\"       11  \n",
       "1   \"Samsung YES\"   \"Nexus NO\"       12  \n",
       "2   \"Samsung YES\"   \"Nexus NO\"        9  \n",
       "3    \"Samsung NO\"   \"Nexus NO\"        2  \n",
       "4   \"Samsung YES\"  \"Nexus YES\"        8  \n",
       "5    \"Samsung NO\"  \"Nexus YES\"       13  \n",
       "6    \"Samsung NO\"   \"Nexus NO\"        7  \n",
       "7    \"Samsung NO\"  \"Nexus YES\"        4  \n",
       "8   \"Samsung YES\"   \"Nexus NO\"        5  \n",
       "9   \"Samsung YES\"  \"Nexus YES\"       16  \n",
       "10  \"Samsung YES\"  \"Nexus YES\"        3  \n",
       "11   \"Samsung NO\"   \"Nexus NO\"        6  \n",
       "12  \"Samsung YES\"   \"Nexus NO\"       10  \n",
       "13   \"Samsung NO\"  \"Nexus YES\"       15  \n",
       "14   \"Samsung NO\"  \"Nexus YES\"        1  \n",
       "15  \"Samsung YES\"  \"Nexus YES\"       14  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjoint_data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Goal \n",
    "\n",
    "The goal is to fit the preference rankings to show how product attributes affect purchasing decisions.\n",
    "\n",
    "### 5) Procedure\n",
    "\n",
    "Product profiles, defined by their attributes, are shown to consumers. By the ranking consumers reveal their preferences for:\n",
    "\n",
    "    - The products \n",
    "    - The corresponding attributes that define products\n",
    "    \n",
    "The computed attribute importance values and part-worths associated with levels of attributes are obtained as a group (or jointly). We will utilize sum contrasts. The sum of the fitted regression coefficients across the levels of each attribute is zero by construction. The fitted regression coefficients represent (conjoint) measures of utility called **part-worths**:\n",
    "\n",
    "     - Part-worths are the coefficients of the regression and measure the utilities.\n",
    "     - Part-worths reflect the strength of individual consumer preferences for each level of each attribute in the study. \n",
    "     - Positive part-worths add to a product's value in the mind of the consumer. \n",
    "     - Negative part-worths subtract from that value. \n",
    "     - Summing across the part-worths of a product, we obtain the utility. \n",
    "     - The relative importance of attributes is defined using the ranges of part-worth within attributes.\n",
    "     \n",
    "The linear model fit to conjoint rankings can be used to predict what the consumer is likely to do about mobile communications in the future. More concretely, we fit a linear model to each individual's ratings or rankings, hence measuring the utility or part-worth of each level of each attribute, as well as the relative importance of attributes. \n",
    "\n",
    "Conjoint measures can be used to predict each individual's choices in the marketplace. and perform marketplace simulations, exploring alternative product designs and pricing policies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdcAAAJVCAYAAACMFL+PAAAYImlDQ1BJQ0MgUHJvZmlsZQAAWIWV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\n+A+ZzygjZk319AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='spinechart.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spine chart shows that:\n",
    "\n",
    "    - Monthly cost has considerable importance. \n",
    "    - Next is 4G availability. \n",
    "    - Start-up cost, since it is a one-time cost, is much less important than monthly cost. \n",
    "    - This consumer ranks the four service providers about equally. \n",
    "    - Having a nearby retail store is not an advantage. \n",
    "    - Consumer probably is an Android user because we see higher importance for service providers that offer Samsung phones and tablets first and Nexus second, while the availability of Apple phones and tablets is irrelevant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Python Code\n",
    "\n",
    "\n",
    "6.1) We set up sum contrasts for effects coding as needed for conjoint analysis using `C(effect, Sum)` notation within main effects model specification\n",
    "\n",
    "6.2) We then fit the linear regression model using the syntax:\n",
    "\n",
    "        model = sm.ols(y,x)\n",
    "        results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>ranking</td>     <th>  R-squared:         </th> <td>   0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   97.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Jan 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0794</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:16:12</td>     <th>  Log-Likelihood:    </th> <td>  10.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   8.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     1</td>      <th>  BIC:               </th> <td>   20.45</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    14</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>    8.5000</td> <td>    0.125</td> <td>   68.000</td> <td> 0.009</td> <td>    6.912    10.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(brand, Sum)[S.\"AT&T\"]</th>         <td> 2.442e-15</td> <td>    0.217</td> <td> 1.13e-14</td> <td> 1.000</td> <td>   -2.751     2.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(brand, Sum)[S.\"T-Mobile\"]</th>     <td>   -0.2500</td> <td>    0.217</td> <td>   -1.155</td> <td> 0.454</td> <td>   -3.001     2.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(brand, Sum)[S.\"US Cellular\"]</th>  <td> 5.551e-15</td> <td>    0.217</td> <td> 2.56e-14</td> <td> 1.000</td> <td>   -2.751     2.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(startup, Sum)[S.\"$100\"]</th>       <td>    0.7500</td> <td>    0.217</td> <td>    3.464</td> <td> 0.179</td> <td>   -2.001     3.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(startup, Sum)[S.\"$200\"]</th>       <td>-6.439e-15</td> <td>    0.217</td> <td>-2.97e-14</td> <td> 1.000</td> <td>   -2.751     2.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(startup, Sum)[S.\"$300\"]</th>       <td> 5.773e-15</td> <td>    0.217</td> <td> 2.67e-14</td> <td> 1.000</td> <td>   -2.751     2.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(monthly, Sum)[S.\"$100\"]</th>       <td>    5.0000</td> <td>    0.217</td> <td>   23.094</td> <td> 0.028</td> <td>    2.249     7.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(monthly, Sum)[S.\"$200\"]</th>       <td>    2.0000</td> <td>    0.217</td> <td>    9.238</td> <td> 0.069</td> <td>   -0.751     4.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(monthly, Sum)[S.\"$300\"]</th>       <td>   -1.2500</td> <td>    0.217</td> <td>   -5.774</td> <td> 0.109</td> <td>   -4.001     1.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(service, Sum)[S.\"4G NO\"]</th>      <td>   -1.7500</td> <td>    0.125</td> <td>  -14.000</td> <td> 0.045</td> <td>   -3.338    -0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(retail, Sum)[S.\"Retail NO\"]</th>   <td>    0.2500</td> <td>    0.125</td> <td>    2.000</td> <td> 0.295</td> <td>   -1.338     1.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(apple, Sum)[S.\"Apple NO\"]</th>     <td>    0.2500</td> <td>    0.125</td> <td>    2.000</td> <td> 0.295</td> <td>   -1.338     1.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(samsung, Sum)[S.\"Samsung NO\"]</th> <td>   -1.1250</td> <td>    0.125</td> <td>   -9.000</td> <td> 0.070</td> <td>   -2.713     0.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(google, Sum)[S.\"Nexus NO\"]</th>    <td>   -0.7500</td> <td>    0.125</td> <td>   -6.000</td> <td> 0.105</td> <td>   -2.338     0.838</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>30.796</td> <th>  Durbin-Watson:     </th> <td>   2.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>   2.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.000</td> <th>  Prob(JB):          </th> <td>   0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.000</td> <th>  Cond. No.          </th> <td>    2.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                ranking   R-squared:                       0.999\n",
       "Model:                            OLS   Adj. R-squared:                  0.989\n",
       "Method:                 Least Squares   F-statistic:                     97.07\n",
       "Date:                Wed, 10 Jan 2018   Prob (F-statistic):             0.0794\n",
       "Time:                        10:16:12   Log-Likelihood:                 10.568\n",
       "No. Observations:                  16   AIC:                             8.864\n",
       "Df Residuals:                       1   BIC:                             20.45\n",
       "Df Model:                          14                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================================\n",
       "                                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------------------\n",
       "Intercept                           8.5000      0.125     68.000      0.009         6.912    10.088\n",
       "C(brand, Sum)[S.\"AT&T\"]          2.442e-15      0.217   1.13e-14      1.000        -2.751     2.751\n",
       "C(brand, Sum)[S.\"T-Mobile\"]        -0.2500      0.217     -1.155      0.454        -3.001     2.501\n",
       "C(brand, Sum)[S.\"US Cellular\"]   5.551e-15      0.217   2.56e-14      1.000        -2.751     2.751\n",
       "C(startup, Sum)[S.\"$100\"]           0.7500      0.217      3.464      0.179        -2.001     3.501\n",
       "C(startup, Sum)[S.\"$200\"]       -6.439e-15      0.217  -2.97e-14      1.000        -2.751     2.751\n",
       "C(startup, Sum)[S.\"$300\"]        5.773e-15      0.217   2.67e-14      1.000        -2.751     2.751\n",
       "C(monthly, Sum)[S.\"$100\"]           5.0000      0.217     23.094      0.028         2.249     7.751\n",
       "C(monthly, Sum)[S.\"$200\"]           2.0000      0.217      9.238      0.069        -0.751     4.751\n",
       "C(monthly, Sum)[S.\"$300\"]          -1.2500      0.217     -5.774      0.109        -4.001     1.501\n",
       "C(service, Sum)[S.\"4G NO\"]         -1.7500      0.125    -14.000      0.045        -3.338    -0.162\n",
       "C(retail, Sum)[S.\"Retail NO\"]       0.2500      0.125      2.000      0.295        -1.338     1.838\n",
       "C(apple, Sum)[S.\"Apple NO\"]         0.2500      0.125      2.000      0.295        -1.338     1.838\n",
       "C(samsung, Sum)[S.\"Samsung NO\"]    -1.1250      0.125     -9.000      0.070        -2.713     0.463\n",
       "C(google, Sum)[S.\"Nexus NO\"]       -0.7500      0.125     -6.000      0.105        -2.338     0.838\n",
       "==============================================================================\n",
       "Omnibus:                       30.796   Durbin-Watson:                   2.000\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                2.667\n",
       "Skew:                           0.000   Prob(JB):                        0.264\n",
       "Kurtosis:                       1.000   Cond. No.                         2.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_effects_model = 'ranking ~ C(brand, Sum) + C(startup, Sum) +  \\\n",
    "    C(monthly, Sum) + C(service, Sum) + C(retail, Sum) + C(apple, Sum) + \\\n",
    "    C(samsung, Sum) + C(google, Sum)'\n",
    "\n",
    "# \n",
    "main_effects_model_fit = smf.ols(main_effects_model, data = conjoint_data_frame).fit()\n",
    "main_effects_model_fit.summary() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.3) We then build a list with the attributes:   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conjoint_attributes = ['brand', 'startup', 'monthly', 'service','retail', 'apple', 'samsung', 'google']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>brand</th>\n",
       "      <th>startup</th>\n",
       "      <th>monthly</th>\n",
       "      <th>service</th>\n",
       "      <th>retail</th>\n",
       "      <th>apple</th>\n",
       "      <th>samsung</th>\n",
       "      <th>google</th>\n",
       "      <th>ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"AT&amp;T\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"$100\"</td>\n",
       "      <td>\"4G NO\"</td>\n",
       "      <td>\"Retail NO\"</td>\n",
       "      <td>\"Apple NO\"</td>\n",
       "      <td>\"Samsung NO\"</td>\n",
       "      <td>\"Nexus NO\"</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    brand startup monthly  service       retail       apple       samsung  \\\n",
       "0  \"AT&T\"  \"$100\"  \"$100\"  \"4G NO\"  \"Retail NO\"  \"Apple NO\"  \"Samsung NO\"   \n",
       "\n",
       "       google  ranking  \n",
       "0  \"Nexus NO\"       11  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjoint_data_frame.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            \"AT&T\"\n",
      "1         \"Verizon\"\n",
      "2     \"US Cellular\"\n",
      "3         \"Verizon\"\n",
      "4         \"Verizon\"\n",
      "5         \"Verizon\"\n",
      "6     \"US Cellular\"\n",
      "7            \"AT&T\"\n",
      "8            \"AT&T\"\n",
      "9        \"T-Mobile\"\n",
      "10    \"US Cellular\"\n",
      "11       \"T-Mobile\"\n",
      "12       \"T-Mobile\"\n",
      "13    \"US Cellular\"\n",
      "14       \"T-Mobile\"\n",
      "15           \"AT&T\"\n",
      "Name: brand, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(conjoint_data_frame.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\"AT&T\"' '\"T-Mobile\"' '\"US Cellular\"' '\"Verizon\"']\n",
      "['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"']\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(conjoint_data_frame.iloc[:,0]))\n",
    "print(list(np.unique(conjoint_data_frame.iloc[:,0])))\n",
    "print(len(list(np.unique(conjoint_data_frame.iloc[:,0]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.4) Next we build part-worth information one attribute at a time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"AT&T\"' '\"T-Mobile\"' '\"US Cellular\"' '\"Verizon\"']\n",
      "4\n",
      "aux is:  ['\"AT&T\"' '\"T-Mobile\"' '\"US Cellular\"' '\"Verizon\"']\n",
      "list(aux) is:  ['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"']]\n",
      "end is: 4\n",
      "new_part_worth is: [2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15]\n",
      "new_part_worth is: [2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944]\n",
      "new_part_worth_range is: [0.50000000000000688]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944]]\n",
      "startup\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"$100\"' '\"$200\"' '\"$300\"' '\"$400\"']\n",
      "4\n",
      "aux is:  ['\"$100\"' '\"$200\"' '\"$300\"' '\"$400\"']\n",
      "list(aux) is:  ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"']]\n",
      "end is: 7\n",
      "new_part_worth is: [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15]\n",
      "new_part_worth is: [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067]]\n",
      "monthly\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"$100\"' '\"$200\"' '\"$300\"' '\"$400\"']\n",
      "4\n",
      "aux is:  ['\"$100\"' '\"$200\"' '\"$300\"' '\"$400\"']\n",
      "list(aux) is:  ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"']]\n",
      "end is: 10\n",
      "new_part_worth is: [4.9999999999999956, 2.0000000000000022, -1.2499999999999978]\n",
      "new_part_worth is: [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75]]\n",
      "service\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"4G NO\"' '\"4G YES\"']\n",
      "2\n",
      "aux is:  ['\"4G NO\"' '\"4G YES\"']\n",
      "list(aux) is:  ['\"4G NO\"', '\"4G YES\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"4G NO\"', '\"4G YES\"']]\n",
      "end is: 11\n",
      "new_part_worth is: [-1.7499999999999996]\n",
      "new_part_worth is: [-1.7499999999999996, 1.7499999999999996]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996, 3.4999999999999991]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75], [-1.7499999999999996, 1.7499999999999996]]\n",
      "retail\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"Retail NO\"' '\"Retail YES\"']\n",
      "2\n",
      "aux is:  ['\"Retail NO\"' '\"Retail YES\"']\n",
      "list(aux) is:  ['\"Retail NO\"', '\"Retail YES\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"4G NO\"', '\"4G YES\"'], ['\"Retail NO\"', '\"Retail YES\"']]\n",
      "end is: 12\n",
      "new_part_worth is: [0.25000000000000089]\n",
      "new_part_worth is: [0.25000000000000089, -0.25000000000000089]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996, 3.4999999999999991, 0.50000000000000178]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75], [-1.7499999999999996, 1.7499999999999996], [0.25000000000000089, -0.25000000000000089]]\n",
      "apple\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"Apple NO\"' '\"Apple YES\"']\n",
      "2\n",
      "aux is:  ['\"Apple NO\"' '\"Apple YES\"']\n",
      "list(aux) is:  ['\"Apple NO\"', '\"Apple YES\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"4G NO\"', '\"4G YES\"'], ['\"Retail NO\"', '\"Retail YES\"'], ['\"Apple NO\"', '\"Apple YES\"']]\n",
      "end is: 13\n",
      "new_part_worth is: [0.24999999999999956]\n",
      "new_part_worth is: [0.24999999999999956, -0.24999999999999956]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996, 3.4999999999999991, 0.50000000000000178, 0.49999999999999911]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75], [-1.7499999999999996, 1.7499999999999996], [0.25000000000000089, -0.25000000000000089], [0.24999999999999956, -0.24999999999999956]]\n",
      "samsung\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"Samsung NO\"' '\"Samsung YES\"']\n",
      "2\n",
      "aux is:  ['\"Samsung NO\"' '\"Samsung YES\"']\n",
      "list(aux) is:  ['\"Samsung NO\"', '\"Samsung YES\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"4G NO\"', '\"4G YES\"'], ['\"Retail NO\"', '\"Retail YES\"'], ['\"Apple NO\"', '\"Apple YES\"'], ['\"Samsung NO\"', '\"Samsung YES\"']]\n",
      "end is: 14\n",
      "new_part_worth is: [-1.1249999999999996]\n",
      "new_part_worth is: [-1.1249999999999996, 1.1249999999999996]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996, 3.4999999999999991, 0.50000000000000178, 0.49999999999999911, 2.2499999999999991]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75], [-1.7499999999999996, 1.7499999999999996], [0.25000000000000089, -0.25000000000000089], [0.24999999999999956, -0.24999999999999956], [-1.1249999999999996, 1.1249999999999996]]\n",
      "google\n",
      "np.unique(conjoint_data_frame[item])) is:  ['\"Nexus NO\"' '\"Nexus YES\"']\n",
      "2\n",
      "aux is:  ['\"Nexus NO\"' '\"Nexus YES\"']\n",
      "list(aux) is:  ['\"Nexus NO\"', '\"Nexus YES\"']\n",
      "level_name is:  [['\"AT&T\"', '\"T-Mobile\"', '\"US Cellular\"', '\"Verizon\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"$100\"', '\"$200\"', '\"$300\"', '\"$400\"'], ['\"4G NO\"', '\"4G YES\"'], ['\"Retail NO\"', '\"Retail YES\"'], ['\"Apple NO\"', '\"Apple YES\"'], ['\"Samsung NO\"', '\"Samsung YES\"'], ['\"Nexus NO\"', '\"Nexus YES\"']]\n",
      "end is: 15\n",
      "new_part_worth is: [-0.75]\n",
      "new_part_worth is: [-0.75, 0.75]\n",
      "new_part_worth_range is: [0.50000000000000688, 1.500000000000002, 10.749999999999996, 3.4999999999999991, 0.50000000000000178, 0.49999999999999911, 2.2499999999999991, 1.5]\n",
      "part_worth is: [[2.4424906541753444e-15, -0.25000000000000744, 5.5511151231257827e-15, 0.24999999999999944], [0.75000000000000133, -6.4392935428259079e-15, 5.773159728050814e-15, -0.75000000000000067], [4.9999999999999956, 2.0000000000000022, -1.2499999999999978, -5.75], [-1.7499999999999996, 1.7499999999999996], [0.25000000000000089, -0.25000000000000089], [0.24999999999999956, -0.24999999999999956], [-1.1249999999999996, 1.1249999999999996], [-0.75, 0.75]]\n"
     ]
    }
   ],
   "source": [
    "conjoint_attributes = ['brand', 'startup', 'monthly', 'service','retail', 'apple', 'samsung', 'google']\n",
    "level_name = []\n",
    "part_worth = []\n",
    "part_worth_range = []\n",
    "end = 1  # initialize index for coefficient in params\n",
    "for item in conjoint_attributes:\n",
    "    print (item)\n",
    "    print (\"np.unique(conjoint_data_frame[item])) is: \", np.unique(conjoint_data_frame[item]))\n",
    "    nlevels = len(list(np.unique(conjoint_data_frame[item])))\n",
    "    print (nlevels)\n",
    "    aux = np.unique(conjoint_data_frame[item])\n",
    "    print (\"aux is: \", aux)\n",
    "    level_name.append(list(aux)) \n",
    "    print (\"list(aux) is: \", list(aux))\n",
    "    print (\"level_name is: \", level_name)\n",
    "    begin = end \n",
    "    end = begin + nlevels - 1\n",
    "    print (\"end is:\", end)\n",
    "    new_part_worth = list(main_effects_model_fit.params[begin:end])\n",
    "    print (\"new_part_worth is:\", new_part_worth)\n",
    "    new_part_worth.append((-1) * sum(new_part_worth)) \n",
    "    print (\"new_part_worth is:\", new_part_worth)\n",
    "    part_worth_range.append(max(new_part_worth) - min(new_part_worth)) \n",
    "    print (\"new_part_worth_range is:\", part_worth_range)\n",
    "    part_worth.append(new_part_worth)   \n",
    "    print (\"part_worth is:\", part_worth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.0\n"
     ]
    }
   ],
   "source": [
    "print(sum(part_worth_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.5) We then compute attribute relative importance values from ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attribute_importance is: [2.38]\n",
      "attribute_importance is: [2.38, 7.14]\n",
      "attribute_importance is: [2.38, 7.14, 51.19]\n",
      "attribute_importance is: [2.38, 7.14, 51.19, 16.67]\n",
      "attribute_importance is: [2.38, 7.14, 51.19, 16.67, 2.38]\n",
      "attribute_importance is: [2.38, 7.14, 51.19, 16.67, 2.38, 2.38]\n",
      "attribute_importance is: [2.38, 7.14, 51.19, 16.67, 2.38, 2.38, 10.71]\n",
      "attribute_importance is: [2.38, 7.14, 51.19, 16.67, 2.38, 2.38, 10.71, 7.14]\n",
      "\n",
      "Attribute: Mobile Service Provider\n",
      "    Importance: 2.38\n",
      "    Level Part-Worths\n",
      "        \"AT&T\" 2.44249065418e-15\n",
      "        \"T-Mobile\" -0.25\n",
      "        \"US Cellular\" 5.55111512313e-15\n",
      "        \"Verizon\" 0.25\n",
      "\n",
      "Attribute: Start-up Cost\n",
      "    Importance: 7.14\n",
      "    Level Part-Worths\n",
      "        \"$100\" 0.75\n",
      "        \"$200\" -6.43929354283e-15\n",
      "        \"$300\" 5.77315972805e-15\n",
      "        \"$400\" -0.75\n",
      "\n",
      "Attribute: Monthly Cost\n",
      "    Importance: 51.19\n",
      "    Level Part-Worths\n",
      "        \"$100\" 5.0\n",
      "        \"$200\" 2.0\n",
      "        \"$300\" -1.25\n",
      "        \"$400\" -5.75\n",
      "\n",
      "Attribute: Offers 4G Service\n",
      "    Importance: 16.67\n",
      "    Level Part-Worths\n",
      "        \"4G NO\" -1.75\n",
      "        \"4G YES\" 1.75\n",
      "\n",
      "Attribute: Has Nearby Retail Store\n",
      "    Importance: 2.38\n",
      "    Level Part-Worths\n",
      "        \"Retail NO\" 0.25\n",
      "        \"Retail YES\" -0.25\n",
      "\n",
      "Attribute: Sells Apple Products\n",
      "    Importance: 2.38\n",
      "    Level Part-Worths\n",
      "        \"Apple NO\" 0.25\n",
      "        \"Apple YES\" -0.25\n",
      "\n",
      "Attribute: Sells Samsung Products\n",
      "    Importance: 10.71\n",
      "    Level Part-Worths\n",
      "        \"Samsung NO\" -1.125\n",
      "        \"Samsung YES\" 1.125\n",
      "\n",
      "Attribute: Sells Google/Nexus Products\n",
      "    Importance: 7.14\n",
      "    Level Part-Worths\n",
      "        \"Nexus NO\" -0.75\n",
      "        \"Nexus YES\" 0.75\n"
     ]
    }
   ],
   "source": [
    "attribute_importance = []\n",
    "for item in part_worth_range:\n",
    "    attribute_importance.append(round(100 * (item / sum(part_worth_range)),2))\n",
    "    print (\"attribute_importance is:\",attribute_importance)\n",
    "# user-defined dictionary for printing descriptive attribute names     \n",
    "effect_name_dict = {'brand' : 'Mobile Service Provider', \\\n",
    "    'startup' : 'Start-up Cost', 'monthly' : 'Monthly Cost', \\\n",
    "    'service' : 'Offers 4G Service', 'retail' : 'Has Nearby Retail Store', \\\n",
    "    'apple' : 'Sells Apple Products', 'samsung' : 'Sells Samsung Products', \\\n",
    "    'google' : 'Sells Google/Nexus Products'}  \n",
    " \n",
    "# report conjoint measures to console \n",
    "index = 0  # initialize for use in for-loop\n",
    "for item in conjoint_attributes:\n",
    "    print('\\nAttribute:', effect_name_dict[item])\n",
    "    print('    Importance:', attribute_importance[index])\n",
    "    print('    Level Part-Worths')\n",
    "    for level in range(len(level_name[index])):\n",
    "        print('       ',level_name[index][level], part_worth[index][level])       \n",
    "    index = index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}