Waze Project

Course 4 - The Power of Statistics

Your team is nearing the midpoint of their user churn project. So far, you’ve completed a project proposal, and used Python to explore and analyze Waze’s user data. You’ve also used Python to create data visualizations. The next step is to use statistical methods to analyze and interpret your data.

You receive a new email from Sylvester Esperanza, your project manager. Sylvester tells your team about a new request from leadership: to analyze the relationship between mean amount of rides and device type. You also discover follow-up emails from three other team members: May Santner, Chidi Ga, and Harriet Hadzic. These emails discuss the details of the analysis. They would like a statistical analysis of ride data based on device type. In particular, leadership wants to know if there is a statistically significant difference in mean amount of rides between iPhone® users and Android™ users. A final email from Chidi includes your specific assignment: to conduct a two-sample hypothesis test (t-test) to analyze the difference in the mean amount of rides between iPhone users and Android users.

A notebook was structured and prepared to help you in this project. Please complete the following questions and prepare an executive summary.

Course 4 End-of-course project: Data exploration and hypothesis testing

In this activity, you will explore the data provided and conduct a hypothesis test.

The purpose of this project is to demostrate knowledge of how to conduct a two-sample hypothesis test.

The goal is to apply descriptive statistics and hypothesis testing in Python.

This activity has three parts:

Part 1: Imports and data loading

  • What data packages will be necessary for hypothesis testing?

Part 2: Conduct hypothesis testing

  • How did computing descriptive statistics help you analyze your data?

  • How did you formulate your null hypothesis and alternative hypothesis?

Part 3: Communicate insights with stakeholders

  • What key business insight(s) emerged from your hypothesis test?

  • What business recommendations do you propose based on your results?


Follow the instructions and answer the questions below to complete the activity. Then, you will complete an Executive Summary using the questions listed on the PACE Strategy Document.

Be sure to complete this activity before moving on. The next course item will provide you with a completed exemplar to compare to your own work.

Data exploration and hypothesis testing

PACE stages

Throughout these project notebooks, you'll see references to the problem-solving framework PACE. The following notebook components are labeled with the respective PACE stage: Plan, Analyze, Construct, and Execute.

PACE: Plan

Consider the questions in your PACE Strategy Document and those below to craft your response:

  1. What is your research question for this data project? Later on, you will need to formulate the null and alternative hypotheses as the first step of your hypothesis test. Consider your research question now, at the start of this task.

==> ENTER YOUR RESPONSE HERE

Complete the following tasks to perform statistical analysis of your data:

Task 1. Imports and data loading

Import packages and libraries needed to compute descriptive statistics and conduct a hypothesis test.

Hint:

 Before you begin, recall the following Python packages and functions: *Main functions*: stats.ttest_ind(a, b, equal_var) *Other functions*: mean() *Packages*: pandas, stats.scipy
In [2]:
# Import any relevant packages or libraries
### YOUR CODE HERE ###
import pandas as pd
import numpy as np

Import the dataset.

Note: As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions.

In [3]:
# Load dataset into dataframe
df = pd.read_csv('waze_dataset.csv')

PACE: Analyze and Construct

Consider the questions in your PACE Strategy Document and those below to craft your response:

  1. Data professionals use descriptive statistics for exploratory data analysis (EDA). How can computing descriptive statistics help you learn more about your data in this stage of your analysis?

==> ENTER YOUR RESPONSE HERE

Task 2. Data exploration

Use descriptive statistics to conduct exploratory data analysis (EDA).

Hint:

 Refer back to *Self Review Descriptive Statistics* for this step-by-step proccess.

Note: In the dataset, device is a categorical variable with the labels iPhone and Android.

In order to perform this analysis, you must turn each label into an integer. The following code assigns a 1 for an iPhone user and a 2 for Android. It assigns this label back to the variable device_new.

Note: Creating a new variable is ideal so that you don't overwrite original data.

  1. Create a dictionary called map_dictionary that contains the class labels ('Android' and 'iPhone') for keys and the values you want to convert them to (2 and 1) as values.

  2. Create a new column called device_type that is a copy of the device column.

  3. Use the map() method on the device_type series. Pass map_dictionary as its argument. Reassign the result back to the device_type series. </br></br> When you pass a dictionary to the Series.map() method, it will replace the data in the series where that data matches the dictionary's keys. The values that get imputed are the values of the dictionary.

Example:
df['column']
column
A
B
A
B 
map_dictionary = {'A': 2, 'B': 1}
df['column'] = df['column'].map(map_dictionary)
df['column']
column
2
1
2
1
In [4]:
# 1. Create `map_dictionary`
### YOUR CODE HERE ###

map_dict = {'Android':2, 'iPhone':1}
# 2. Create new `device_type` column
### YOUR CODE HERE ###

df['device_type'] = df['device'].map(map_dict)
# 3. Map the new column to the dictionary
### YOUR CODE HERE ###

df.head()
Out[4]:
ID label sessions drives total_sessions n_days_after_onboarding total_navigations_fav1 total_navigations_fav2 driven_km_drives duration_minutes_drives activity_days driving_days device device_type
0 0 retained 283 226 296.748273 2276 208 0 2628.845068 1985.775061 28 19 Android 2
1 1 retained 133 107 326.896596 1225 19 64 13715.920550 3160.472914 13 11 iPhone 1
2 2 retained 114 95 135.522926 2651 0 0 3059.148818 1610.735904 14 8 Android 2
3 3 retained 49 40 67.589221 15 322 7 913.591123 587.196542 7 3 iPhone 1
4 4 retained 84 68 168.247020 1562 166 5 3950.202008 1219.555924 27 18 Android 2

You are interested in the relationship between device type and the number of drives. One approach is to look at the average number of drives for each device type. Calculate these averages.

In [5]:
### YOUR CODE HERE ###

df.groupby('device_type')['drives'].mean()
Out[5]:
device_type
1    67.859078
2    66.231838
Name: drives, dtype: float64

Based on the averages shown, it appears that drivers who use an iPhone device to interact with the application have a higher number of drives on average. However, this difference might arise from random sampling, rather than being a true difference in the number of drives. To assess whether the difference is statistically significant, you can conduct a hypothesis test.

Task 3. Hypothesis testing

Your goal is to conduct a two-sample t-test. Recall the steps for conducting a hypothesis test:

  1. State the null hypothesis and the alternative hypothesis
  2. Choose a signficance level
  3. Find the p-value
  4. Reject or fail to reject the null hypothesis

Note: This is a t-test for two independent samples. This is the appropriate test since the two groups are independent (Android users vs. iPhone users).

Recall the difference between the null hypothesis ($H_0$) and the alternative hypothesis ($H_A$).

Question: What are your hypotheses for this data project?

==> ENTER YOUR RESPONSE HERE

Next, choose 5% as the significance level and proceed with a two-sample t-test.

You can use the stats.ttest_ind() function to perform the test.

Technical note: The default for the argument equal_var in stats.ttest_ind() is True, which assumes population variances are equal. This equal variance assumption might not hold in practice (that is, there is no strong reason to assume that the two groups have the same variance); you can relax this assumption by setting equal_var to False, and stats.ttest_ind() will perform the unequal variances $t$-test (known as Welch's t-test). Refer to the scipy t-test documentation for more information.

  1. Isolate the drives column for iPhone users.
  2. Isolate the drives column for Android users.
  3. Perform the t-test
In [8]:
# 1. Isolate the `drives` column for iPhone users.
### YOUR CODE HERE ###
from scipy import stats

iphone = df[df['device_type']==1] ['drives']
# 2. Isolate the `drives` column for Android users.
### YOUR CODE HERE ###

android = df[df['device_type']==2]['drives']
# 3. Perform the t-test
### YOUR CODE HERE ###

stats.ttest_ind(a=iphone, b=android, equal_var=False)
Out[8]:
Ttest_indResult(statistic=1.4635232068852353, pvalue=0.1433519726802059)

Question: Based on the p-value you got above, do you reject or fail to reject the null hypothesis?

==> ENTER YOUR RESPONSE HERE

PACE: Execute

Consider the questions in your PACE Strategy Document to reflect on the Execute stage.

Task 4. Communicate insights with stakeholders

Now that you've completed your hypothesis test, the next step is to share your findings with the Waze leadership team. Consider the following question as you prepare to write your executive summary:

  • What business insight(s) can you draw from the result of your hypothesis test?

==> ENTER YOUR RESPONSE HERE

Congratulations! You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the "save" icon at the top of this notebook to ensure your work has been logged.