Analyse A/B Test Results

The Project

For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who “convert,” meaning the number of users who decide to pay for the company’s product. My goal is to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.

The analysis is conducted using Jupyter Notebook running on a Python kernel.

What We Learned

• Calculating probability using proportions

• How to articulate hypothesis statements using statistical terminology

• Using the statsmodel module to simulate experiments

• Calculating difference of means between an empirical distribution and a hypothetical distribution

• Plotting differences using a histogram, with reference lines

• Using logistic regression to determine probabilities for one of two possible outcomes

• Creating dummy variables for making categorical variables usable in a regression model

• Creating interaction variables to represent a combination of variables

• Interpreting regression results accurately to make valid conclusions

The Code and the Report

• GitHub repository for the data and the Jupyter Notebook

• the PDF report can also be found here

References

• https://towardsdatascience.com/introduction-to-linear-regression-in-python-c12a072bedf0

• https://stackoverflow.com/questions/12146914/what-is-the-difference-between-linear-regression-and-logistic-regression