A company has developed a new e-commerce 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.
The main goal of this project was to understand the results of an A/B test run by the website and provide statistical and practical interpretation on the test results.
There was no sufficient evidence found to suggest that the new page converts better than the old one.
- Bootstrapping sampling distributions
- P-value and Z-score calculation
- Logistic regression
- Python, Pandas, Numpy, Matplotlib, StatsModels, Scipy
- Jupyter Notebook