2_SampleAnalysis_ABtest.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2_SampleAnalysis_ABtest.ipynb","provenance":[{"file_id":"1Ov5KW-L1BtvPotdqJzEDsOtVCmot5nV_","timestamp":1578322982190},{"file_id":"1vFy9KQ5ILPKPp-1wNjNtZprg_vr9Goqd","timestamp":1573158327351}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"cells":[{"cell_type":"markdown","metadata":{"id":"_CSmfo4Yo3uY"},"source":["# QSO370 / QSO570 A Sample Analysis"]},{"cell_type":"markdown","metadata":{"id":"tq_OcXIMo_dr"},"source":["This notebook serves as a quick introduction to the types of things we are trying to do with predictive analytics as well as a first introduction to Python and the Jupyter Notebook environment. In QSO370/QSO570 we'll focus on answering business-related questions to help improve various facets of the business environment (customer/employee retention, testing efficiencies of marketing campaigns, predicting sales, price/rate setting, and more). We'll use various tools in order to answer these questions and aid in informed decision-making -- some of those tools will involve advanced techniques such as predictive modeling or classification, standard statistical tools like hypothesis testing and confidence intervals, or even simple tools like numerical or graphical summaries of data.\n","\n","In this first notebook, we explore A/B testing. The basic idea behind A/B testing is that a controlled experiment is run, where two or more options are tested against one another (for example, option A versus option B). Such a test helps to prevent a company from rolling out a poor product on a large scale. For example, maybe we are helping a company test which of two possible website layouts leads to the best user satisfaction. When a person lands on our web-page they are randomly shown one of the two layouts under consideration, and then they are asked a few questions about their experience before navigating away. We then run simple hypothesis tests to determine which layout is preferred by our web traffic.\n","\n","Remember that I am assuming no coding background in this course. Please do not worry if the Python code you are seeing looks unfamiliar to you. Our next notebook will be a primer in Python. That being said, you should look through the code and see if you are able to understand what is being done by each code block. For now, all of the code is present and has been executed. Please feel free to experiment with the code blocks if you would like -- double-clicking on a code block gives you edit access; you can run a block of code by pressing *Shift+Enter* simultaneously; don't worry, you can't \"break\" anything beyond repair. While I am not expecting the Python code to be familiar to you, I am expecting that much of the remaining statistics content seems familiar to you. You should be on the lookout for the following items:\n","+ Exploratory data analysis\n","    + Computing and interpreting summary statistics (mean, median, standard deviation, quantiles) for numerical data.\n","    + Computing and interpreting summary statistics (frequency tables) for categorical data.\n","+ Data visualization\n","    + Constructing and interpreting appropriate plots for single numerical and categorical variables, as well as multi-dimensional plots for comparing two or more variables.\n","+ Using sample statistics and data visualizations to compare subgroups (here, the weeks and locations corresponding to the different promotions).\n","+ The use of an ANOVA test to compare more than two group means.\n","+ The use of $t$-tests to compare two group means.\n","\n","If any of these items seem unfamiliar to you, please make an appointment to meet with me. We can discuss the background work you'll need to do in order to be successful in our course. I have resources that I can share with you."]},{"cell_type":"markdown","metadata":{"id":"sWgq8ijqfGVl"},"source":["# A/B Testing Marketing Promotions\n","\n","Which promotion was the most effective?\n","\n","**Scenario:**\n","\n","A fast food chain plans to add a new item to its menu. However, they are still undecided between three possible marketing campaigns for promoting the new product. In order to determine which promotion has the greatest effect on sales, the new item is introduced at locations in several randomly selected markets. A different promotion is used at each location, and the weekly sales of the new item are recorded for the first four weeks\n","\n","**The description of the data set:**\n","Our data set consists of 548 entries including:\n","- `MarketId`: an in-house tag used to describe market types, we won't be using it\n","- `AgeOfStores`: Age of store in years (1–28). The mean age of a store is 8.5 years.\n","- `LocationID`: Unique identifier for store location. Each location is identified by a number. The total number of stores is 137.\n","- `Promotion`: One of three promotions that were tested (1, 2, 3). We don’t really know the specifics of each promotion, nor do we need to.\n","- `Sales` in Thousands: Sales amount for a specific LocationID, Promotion and week. The mean amount of sales are 53.5 thousand dollars.\n","- `Market size`: there are three types of market size: small, medium and large.\n","- `Week`: One of four weeks when the promotions were run (1–4).\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"JFrtn4mCiqyP","executionInfo":{"status":"ok","timestamp":1610479286316,"user_tz":300,"elapsed":1228,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj1KWqLKG2SAQxx8KvqzqhM94EvqxkXs2iOw7gQWQ=s64","userId":"02074648007699947871"}},"outputId":"3092896a-d496-4305-ae0e-1d39949eb82c"},"source":["#Don't worry so much about the code for today -- Our next notebook will be a Python primer, \n","#and we'll get much more exposure throughout the course of the semester.\n","\n","#Load relevant libraries (pandas is for working with tables and matplotlib is for plotting)\n","#pd is a standard alias for pandas (so that we can type pd.method() instead of pandas.method())\n","#when calling pandas functionality. Similarly, plt is a standard alias for matplotlib's pyplot\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","#Read the data from its location on the web\n","#file_name = \"https://raw.githubusercontent.com/rajeevratan84/datascienceforbusiness/master/WA_Fn-UseC_-Marketing-Campaign-Eff-UseC_-FastF.csv\"\n","df = pd.read_csv(\"https://raw.githubusercontent.com/rajeevratan84/datascienceforbusiness/master/WA_Fn-UseC_-Marketing-Campaign-Eff-UseC_-FastF.csv\")\n","\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>MarketID</th>\n","      <th>MarketSize</th>\n","      <th>LocationID</th>\n","      <th>AgeOfStore</th>\n","      <th>Promotion</th>\n","      <th>week</th>\n","      <th>SalesInThousands</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Medium</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>33.73</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Medium</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>35.67</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>Medium</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>29.03</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>Medium</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>39.25</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>Medium</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>27.81</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   MarketID MarketSize  LocationID  ...  Promotion  week  SalesInThousands\n","0         1     Medium           1  ...          3     1             33.73\n","1         1     Medium           1  ...          3     2             35.67\n","2         1     Medium           1  ...          3     3             29.03\n","3         1     Medium           1  ...          3     4             39.25\n","4         1     Medium           2  ...          2     1             27.81\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KITelI1QiqyW","executionInfo":{"status":"ok","timestamp":1610479329246,"user_tz":300,"elapsed":782,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj1KWqLKG2SAQxx8KvqzqhM94EvqxkXs2iOw7gQWQ=s64","userId":"02074648007699947871"}},"outputId":"519889c8-802f-46b1-9f00-b0a08204fb38"},"source":["#We print some basic information about the collected data.\n","print(\"Rows     : \" , df.shape[0])\n","print(\"Columns  : \" , df.shape[1])\n","print(\"\\n Features : \\n\", df.columns.tolist())\n","print(\"\\n Missing values : \", df.isnull().sum().values.sum(), \"\\n\")\n","print(\"Unique values :  \\n\", df.nunique())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Rows     :  548\n","Columns  :  7\n","\n"," Features : \n"," ['MarketID', 'MarketSize', 'LocationID', 'AgeOfStore', 'Promotion', 'week', 'SalesInThousands']\n","\n"," Missing values :  0 \n","\n","Unique values :  \n"," MarketID             10\n","MarketSize            3\n","LocationID          137\n","AgeOfStore           25\n","Promotion             3\n","week                  4\n","SalesInThousands    517\n","dtype: int64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":288},"id":"nXf1B035hQg7","executionInfo":{"status":"ok","timestamp":1610479721904,"user_tz":300,"elapsed":953,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj1KWqLKG2SAQxx8KvqzqhM94EvqxkXs2iOw7gQWQ=s64","userId":"02074648007699947871"}},"outputId":"0f8fa5bd-7a1a-4a9c-d08b-ff4f2b7a46da"},"source":["#The .describe() method gives us a summary for all of the numeric columns in our dataset\n","df.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>MarketID</th>\n","      <th>LocationID</th>\n","      <th>AgeOfStore</th>\n","      <th>Promotion</th>\n","      <th>week</th>\n","      <th>SalesInThousands</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>548.000000</td>\n","      <td>548.000000</td>\n","      <td>548.000000</td>\n","      <td>548.000000</td>\n","      <td>548.000000</td>\n","      <td>548.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>5.715328</td>\n","      <td>479.656934</td>\n","      <td>8.503650</td>\n","      <td>2.029197</td>\n","      <td>2.500000</td>\n","      <td>53.466204</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.877001</td>\n","      <td>287.973679</td>\n","      <td>6.638345</td>\n","      <td>0.810729</td>\n","      <td>1.119055</td>\n","      <td>16.755216</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>17.340000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3.000000</td>\n","      <td>216.000000</td>\n","      <td>4.000000</td>\n","      <td>1.000000</td>\n","      <td>1.750000</td>\n","      <td>42.545000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>6.000000</td>\n","      <td>504.000000</td>\n","      <td>7.000000</td>\n","      <td>2.000000</td>\n","      <td>2.500000</td>\n","      <td>50.200000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>8.000000</td>\n","      <td>708.000000</td>\n","      <td>12.000000</td>\n","      <td>3.000000</td>\n","      <td>3.250000</td>\n","      <td>60.477500</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>10.000000</td>\n","      <td>920.000000</td>\n","      <td>28.000000</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","      <td>99.650000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         MarketID  LocationID  ...        week  SalesInThousands\n","count  548.000000  548.000000  ...  548.000000        548.000000\n","mean     5.715328  479.656934  ...    2.500000         53.466204\n","std      2.877001  287.973679  ...    1.119055         16.755216\n","min      1.000000    1.000000  ...    1.000000         17.340000\n","25%      3.000000  216.000000  ...    1.750000         42.545000\n","50%      6.000000  504.000000  ...    2.500000         50.200000\n","75%      8.000000  708.000000  ...    3.250000         60.477500\n","max     10.000000  920.000000  ...    4.000000         99.650000\n","\n","[8 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"IaP0HR7TtXqb"},"source":["Notice that `Promotion` is interpreted as a numeric, but it shouldn't be. The same is true for `MarketID`, `MarketSize`, `LocationID`, and possibly `Week`. We can get appropriate summaries for these columns via a frequency table with the `.value_counts()` method."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XWvRr_DWt-qn","executionInfo":{"status":"ok","timestamp":1610479724273,"user_tz":300,"elapsed":909,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj1KWqLKG2SAQxx8KvqzqhM94EvqxkXs2iOw7gQWQ=s64","userId":"02074648007699947871"}},"outputId":"c2230aa6-e9be-4863-eeea-a0f3dd1d98c1"},"source":["print(\"MarketID:\\n\", df[\"MarketID\"].value_counts(), \"\\n\")\n","print(\"MarketSize:\\n\", df[\"MarketSize\"].value_counts(), \"\\n\")\n","print(\"LocationID:\\n\", df[\"LocationID\"].value_counts(), \"\\n\")\n","print(\"Promotion:\\n\", df[\"Promotion\"].value_counts(), \"\\n\")\n","print(\"Week:\\n\", df[\"week\"].value_counts())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["MarketID:\n"," 3     88\n","10    80\n","7     60\n","6     60\n","5     60\n","1     52\n","8     48\n","9     40\n","4     36\n","2     24\n","Name: MarketID, dtype: int64 \n","\n","MarketSize:\n"," Medium    320\n","Large     168\n","Small      60\n","Name: MarketSize, dtype: int64 \n","\n","LocationID:\n"," 920    4\n","302    4\n","308    4\n","307    4\n","306    4\n","      ..\n","702    4\n","703    4\n","704    4\n","705    4\n","1      4\n","Name: LocationID, Length: 137, dtype: int64 \n","\n","Promotion:\n"," 3    188\n","2    188\n","1    172\n","Name: Promotion, dtype: int64 \n","\n","Week:\n"," 4    137\n","3    137\n","2    137\n","1    137\n","Name: week, dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"w3UUOK8EvXBY"},"source":["What can we say from the above output? Are there any concerns in moving forward with the test?"]},{"cell_type":"markdown","metadata":{"id":"lc6EtBPmT2jt"},"source":["# EDA and Visualizations"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":512},"id":"doSO_gqViqyZ","executionInfo":{"elapsed":537,"status":"ok","timestamp":1578329227020,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"3cf9cf59-aafc-4468-a22b-27d1bdeaa742"},"source":["# Create some plots to visual our sales and marketing data\n","ax = df.groupby('Promotion').sum()['SalesInThousands'].plot.bar(figsize=(8,8))\n","ax.set_ylabel('Sales (in $1,000)')\n","ax.set_title('Sales Distribution Across the 3 Different Promotions')\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":196},"id":"KQ_wDqmWSwLW","executionInfo":{"elapsed":347,"status":"ok","timestamp":1578329249787,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"bfe2b6c9-96a2-431b-cea0-8caac587e659"},"source":["# Now let's view the promotions for each market size\n","## Note that the `.count()` method returns a count of the number of\n","## entries in each column -- since we have no missing data here, the\n","## counts are all identical (you can see this by removing [\"MarketID\"]\n","## from the code below and re-running the cell). The purpose of \n","## [\"MarketID\"] is to print only a single column of counts, removing\n","## the redundancy.\n","df.groupby(['Promotion', 'MarketSize']).count()[\"MarketID\"]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Promotion  MarketSize\n","1          Large          56\n","           Medium         96\n","           Small          20\n","2          Large          64\n","           Medium        108\n","           Small          16\n","3          Large          48\n","           Medium        116\n","           Small          24\n","Name: MarketID, dtype: int64"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"7XsltrtKiOf7","executionInfo":{"elapsed":511,"status":"ok","timestamp":1578329292119,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"83232c19-19bb-4939-cba5-338d49fefa67"},"source":["# We can get a more intuitive view using the .unstack() method\n","df.groupby(['Promotion', 'MarketSize']).count()['MarketID'].unstack('MarketSize')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>MarketSize</th>\n","      <th>Large</th>\n","      <th>Medium</th>\n","      <th>Small</th>\n","    </tr>\n","    <tr>\n","      <th>Promotion</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>56</td>\n","      <td>96</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>64</td>\n","      <td>108</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>48</td>\n","      <td>116</td>\n","      <td>24</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["MarketSize  Large  Medium  Small\n","Promotion                       \n","1              56      96     20\n","2              64     108     16\n","3              48     116     24"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":622},"id":"SoJNWkPAiqyc","executionInfo":{"elapsed":1861,"status":"ok","timestamp":1573997966382,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64","userId":"08597265227091462140"},"user_tz":0},"outputId":"3467fbfc-2ecd-4ceb-ca20-9577fbcf58f3"},"source":["# Put this into a plot\n","ax = df.groupby(['Promotion', 'MarketSize']).count()['MarketID'].unstack('MarketSize').plot(\n","    kind='bar',\n","    figsize=(12,10),\n","    grid=True)\n","\n","ax.set_ylabel('count')\n","ax.set_title('breakdowns of market sizes across different promotions')\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":622},"id":"v9_I9SSoiqyf","executionInfo":{"elapsed":668,"status":"ok","timestamp":1578329477594,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"c36a2bb1-ebd0-4d91-b454-b6554099f283"},"source":["# Stacking the bars can make it a bit easier compare proportions rather than raw counts -- note that this plot still shows raw counts though.\n","ax = df.groupby(['Promotion', 'MarketSize']).count()['MarketID'].unstack('MarketSize').plot(\n","    kind='bar',\n","    figsize=(12,10),\n","    grid=True,\n","    stacked=True)\n","\n","ax.set_ylabel('count')\n","ax.set_title('breakdowns of market sizes across different promotions')\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"id":"ALayjDr3iqyh","executionInfo":{"elapsed":728,"status":"ok","timestamp":1578335175458,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"9705bff1-593e-48b9-89a0-e93d4a78aa1f"},"source":["ax = df.groupby('AgeOfStore').count()['MarketID'].plot(\n","    kind='bar', \n","    figsize=(12,7),\n","    grid=True)\n","\n","ax.set_xlabel('age')\n","ax.set_ylabel('count')\n","ax.set_title('Overall Distributions Store Ages')\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x504 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":795},"id":"XEnSLS1eUQHm","executionInfo":{"elapsed":498,"status":"ok","timestamp":1578335218163,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"9b777417-28ee-4bf5-985b-47cd77dd1d6e"},"source":["# Group by Age of Store and Promotion to get counts\n","df.groupby(['AgeOfStore', 'Promotion']).count()['MarketID'].unstack()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Promotion</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","    </tr>\n","    <tr>\n","      <th>AgeOfStore</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>24.0</td>\n","      <td>36.0</td>\n","      <td>20.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>8.0</td>\n","      <td>8.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16.0</td>\n","      <td>12.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>16.0</td>\n","      <td>12.0</td>\n","      <td>16.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>8.0</td>\n","      <td>12.0</td>\n","      <td>24.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>20.0</td>\n","      <td>4.0</td>\n","      <td>12.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>4.0</td>\n","      <td>24.0</td>\n","      <td>12.0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>12.0</td>\n","      <td>8.0</td>\n","      <td>20.0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>8.0</td>\n","      <td>12.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>NaN</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","      <td>12.0</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>12.0</td>\n","      <td>4.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>12.0</td>\n","      <td>8.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>NaN</td>\n","      <td>8.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>8.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>4.0</td>\n","      <td>8.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Promotion      1     2     3\n","AgeOfStore                  \n","1           24.0  36.0  20.0\n","2            8.0   8.0   4.0\n","3           16.0  12.0   4.0\n","4           16.0  12.0  16.0\n","5            8.0  12.0  24.0\n","6           20.0   4.0  12.0\n","7            4.0  24.0  12.0\n","8           12.0   8.0  20.0\n","9            8.0  12.0   8.0\n","10           NaN  16.0   8.0\n","11           4.0   NaN  12.0\n","12          12.0   4.0   8.0\n","13          12.0   8.0   NaN\n","14           NaN   8.0   4.0\n","15           4.0   4.0   NaN\n","17           NaN   NaN   4.0\n","18           8.0   NaN   NaN\n","19           4.0   8.0   8.0\n","20           NaN   NaN   4.0\n","22           4.0   NaN   8.0\n","23           NaN   4.0   4.0\n","24           4.0   NaN   8.0\n","25           NaN   4.0   NaN\n","27           4.0   NaN   NaN\n","28           NaN   4.0   NaN"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"yHZPohk6iqyk","executionInfo":{"elapsed":2224,"status":"ok","timestamp":1573997968059,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64","userId":"08597265227091462140"},"user_tz":0},"outputId":"8e257555-930a-4f8c-aed9-e18c5c4fb8b4"},"source":["# Visaulize this summary (note that the .iloc[::-1] is here to reverse the order of the y-axis so that 1 year appears at the top)\n","ax = df.groupby(['AgeOfStore', 'Promotion']).count()['MarketID'].unstack('Promotion').iloc[::-1].plot(\n","    kind='barh', \n","    figsize=(14,18),\n","    grid=True)\n","\n","ax.set_ylabel('age')\n","ax.set_xlabel('count')\n","ax.set_title('overall distributions of age of store')\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x1296 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"O7nhk6hqiqyp","executionInfo":{"elapsed":444,"status":"ok","timestamp":1578335551433,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"4c06bd53-5de4-4db5-836d-c8cd2039412d"},"source":["df.groupby('Promotion').describe()['AgeOfStore']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","    <tr>\n","      <th>Promotion</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>172.0</td>\n","      <td>8.279070</td>\n","      <td>6.636160</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>6.0</td>\n","      <td>12.0</td>\n","      <td>27.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>188.0</td>\n","      <td>7.978723</td>\n","      <td>6.597648</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>7.0</td>\n","      <td>10.0</td>\n","      <td>28.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>188.0</td>\n","      <td>9.234043</td>\n","      <td>6.651646</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>8.0</td>\n","      <td>12.0</td>\n","      <td>24.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           count      mean       std  min  25%  50%   75%   max\n","Promotion                                                      \n","1          172.0  8.279070  6.636160  1.0  3.0  6.0  12.0  27.0\n","2          188.0  7.978723  6.597648  1.0  3.0  7.0  10.0  28.0\n","3          188.0  9.234043  6.651646  1.0  5.0  8.0  12.0  24.0"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"FKSn-54vE8tG"},"source":["This last table makes it easy to understand the overall store age distribution from our summary stats. All test groups have similar age profiles and the average store ages  is ~8 to 9 years old for theese 3 groups. The majority of the stores are 10–12 years old or even younger. We can see that the store profiles within each group are similar to each other. This indicates that our sample groups are well controlled and the A/B testing results will be trustworthy."]},{"cell_type":"markdown","metadata":{"id":"O9WCo0N6RP0j"},"source":["We are really interested in the relationship between promotion type and sales volume (in thousands of dollars here). Let's visualize that relationship (or lackthereof) and then move forward with a statistical test to determine whether we have evidence to suggest a truly superior promotion type."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":562},"id":"zYzpLVEmLLFc","executionInfo":{"elapsed":885,"status":"ok","timestamp":1578337806060,"user":{"displayName":"Adam Gilbert","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDQ8K_t2bmWgnSB3UWY2INXq568pNYg09t4sKd8=s64","userId":"02074648007699947871"},"user_tz":300},"outputId":"5d12e9fd-f209-4aac-b59b-e9905ecfa75c"},"source":["import seaborn as sns\n","import numpy as np\n","\n","\n","_, bins = np.histogram(df[\"SalesInThousands\"])\n","plt.figure(figsize = (12, 15))\n","\n","g = sns.FacetGrid(df, hue=\"Promotion\")\n","g = g.map(sns.distplot, \"SalesInThousands\", bins=bins)\n","g.fig.set_figwidth(12)\n","g.fig.set_figheight(9)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x1080 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x648 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"APnBnPBKUz2y"},"source":["# Performing A/B Testing"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":277},"id":"5TBx6yEXiqys","executionInfo":{"elapsed":580,"status":"ok","timestamp":1573998191625,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64","userId":"08597265227091462140"},"user_tz":0},"outputId":"21492f62-5a1e-4d47-e271-b5179d2337d4"},"source":["means = df.groupby('Promotion').mean()['SalesInThousands']\n","stds = df.groupby('Promotion').std()['SalesInThousands']\n","counts = df.groupby('Promotion').count()['SalesInThousands']\n","print(\"Means:\\n\", means)\n","print(\"Standard Deviations:\\n\", stds)\n","print(\"Sample Sizes:\\n\", counts)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Means:\n"," Promotion\n","1    58.099012\n","2    47.329415\n","3    55.364468\n","Name: SalesInThousands, dtype: float64\n","Standard Deviations:\n"," Promotion\n","1    16.553782\n","2    15.108955\n","3    16.766231\n","Name: SalesInThousands, dtype: float64\n","Sample Sizes:\n"," Promotion\n","1    172\n","2    188\n","3    188\n","Name: SalesInThousands, dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nFhU5duklyC2"},"source":["Recall from introductory statistics that a one-way ANOVA test can be used to determine whether evidence of a significant difference between at least one of these pairs exists. The test statistic for an ANOVA test is an F-statistic (coming from the f-distribution). Large test statistics are associated with small p-values. Remember that a p-value denotes the probability that results at least as favorable to the alternative hypothesis (claiming a difference in sales due to promotion type) would have been observed just by random chance if all of the promotion mean sales were indeed equal (that is, if the null hypothesis were true).\n","If we let $\\mu$ denote average sales, then we test the following hypotheses with our ANOVA test.\n","$$\\begin{array}{ll} H_0: & \\mu_{\\text{Promotion 1}} = \\mu_{\\text{Promotion 2}} = \\mu_{\\text{Promotion 3}}\\\\ H_a: & \\text{The average sales corresponding to at least one of the promotions differs from the others.}\\end{array}$$"]},{"cell_type":"code","metadata":{"id":"Q9_mxoiq3oN4","outputId":"f72a2a31-6d48-4c96-f07e-f89babe6f54e"},"source":["# We conduct ANOVA and other statistical tests with scipy (scientific python) \n","from scipy import stats\n","\n","f, p = stats.f_oneway(df.loc[df[\"Promotion\"] == 1, \"SalesInThousands\"], \n","                      df.loc[df[\"Promotion\"] == 2, \"SalesInThousands\"], \n","                      df.loc[df[\"Promotion\"] == 3, \"SalesInThousands\"])\n","\n","print(\"f-value = \", f)\n","print(\"p-value = \", p)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["f-value =  21.953485793080677\n","p-value =  6.765849261408714e-10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4Zvp7Uyd3oN_"},"source":["Since the p-value is very small (much less than 0.05), we have evidence that suggests at least one of the means is different from the others. Let's go looking for that pair. Remember that we can compare means of two groups using a simple t-test."]},{"cell_type":"markdown","metadata":{"id":"unxLmuGYGSZ2"},"source":["### Analysis of p-values\n"," Our **P-Value is close to 0** which suggests that there is good evidence to **REJECT the Null Hypothesis**. In statistical language, we have significant evidence to suggest a difference between the mean sales in at least two of our groups. Our threshold rejectings the Null Hypothesis is usually less than 0.05."]},{"cell_type":"markdown","metadata":{"id":"BgAgZHlAVRsS"},"source":["# Comparing Promotion 1 vs Promotion 2 in an A/B Test"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"9viIzw-Tiqyz","executionInfo":{"elapsed":593,"status":"ok","timestamp":1573998248738,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64","userId":"08597265227091462140"},"user_tz":0},"outputId":"007fc8b4-0fc0-4a1f-c352-65b7565a9899"},"source":["t, p = stats.ttest_ind(df.loc[df['Promotion'] == 1, 'SalesInThousands'].values,\n","                       df.loc[df['Promotion'] == 2, 'SalesInThousands'].values, \n","                       equal_var=False)\n","print(\"t-value = \" +str(t))\n","print(\"p-value = \" +str(p))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["t-value = 6.42752867090748\n","p-value = 4.2903687179871785e-10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KILTRh3v3oOE"},"source":["Our t-test comes back with a very small p-value, indicating little chance of observing samples whose means are this different if the population means are truly equal. I wonder if the other pairs differ as well."]},{"cell_type":"markdown","metadata":{"id":"c5Li9WaMVfZF"},"source":["# Comparing Promotion 1 vs Promotion 3 in an A/B Test\n","\n","However, if we run a t-test between the promotion group 1 and promotion group 3, we see different results:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"SLLtch3xiqy4","executionInfo":{"elapsed":640,"status":"ok","timestamp":1573998527053,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64","userId":"08597265227091462140"},"user_tz":0},"outputId":"62ed7463-5720-4450-9b89-4b077af12649"},"source":["t, p = stats.ttest_ind(\n","    df.loc[df['Promotion'] == 1, 'SalesInThousands'].values, \n","    df.loc[df['Promotion'] == 3, 'SalesInThousands'].values, \n","    equal_var=False)\n","\n","print(\"t-value = \" +str(t))\n","print(\"p-value = \" +str(p))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["t-value = 1.5560224307758634\n","p-value = 0.12059147742229478\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"QURnWYZmKqDE"},"source":["# Analysis of P and t-values\n","\n","We note that the average sales from promotion group 1 (58.1) is  higher than those from promotion group 3 (55.36). However, running a t-test between these two groups, gives us a t-statistic of 1.556 and a p-value of 0.121. The computed **p-value is a lot higher than 0.05**, above the threshold for statistical significance."]},{"cell_type":"markdown","metadata":{"id":"kmcNk09VJGxl"},"source":["### What should we do?\n","\n","We have significant evidence that suggests Promotion 2 is inferior (at least in comparison to Promotion 1). Let's discontinue Promotion 2. We have more work to do in regard to comparing Promotions 1 and 3 -- it is unclear whether Promotion 1 is truly better than Promotion 3 or if this observed difference was merely due to sampling variation. Would we likely observe similar results if we were to perform this experiment on a new random sampling of stores? Yes in the case of Promotion 1 versus Promotion 2, but unclear in the case of Promotion 1 versus Promotion 3."]},{"cell_type":"code","metadata":{"id":"LrgSl-KO3oOM"},"source":[""],"execution_count":null,"outputs":[]}]}