This is a collection of AB testing methodologies and case studies.
This project was completed as part of Udacity's Data Analyst Nanodegree certification.
Project Objective:
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An e-commerce company has developed a new web page in order to increase the number of "converted" users - users who make purchases on the website.
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The goal of this project is to understand the results of an A/B test on their new and old websites and provide statistical and practical interpretation on the test results.
Analytical Scope:
- Sampling simulation and visulization
- Inferential Statistics: t-test, z-score and p-value
- Logistic regression
Data:
- ab_data.csv
- countries.csv
Document:
- Jupyter notebook: Analyze_AB_Test_Results.ipynb
This is a walk through of the tutorial on Chi-squared testing, based on randomly generated demographic data.
Analytical Scope:
- Chi-squared testing (one-dimensional table)
- Chi-squared testing of independence (two-dimensional table)
Document:
- Jupyter notebook: Chi-squared_ab_testing.ipynb
- Reference: Python for Data Analysis: Chi-Squared Tests
This is a walk through of the tutorial on ANOVA testing, based on randomly generated demographic data.
Analytical Scope:
- One-way ANOVA
- Post-hoc testing
- Bonferroni correction
- Tukey test
Document:
- Jupyter notebook: Analysis_of_Variance (ANOVA).ipynb
- Reference: Python for Data Analysis: Analysis of Variance