This repository contains a comprehensive Jupyter/Colab notebook demonstrating various statistical analysis techniques applied to real-world business scenarios.
- Language: Python
- Libraries: Pandas, NumPy, SciPy.stats, Matplotlib, Seaborn
The analysis is divided into four key tasks:
- Mobile App Rating Analysis: Construction of PMF and CDF for App Store reviews, including expected value and variance calculations.
- Delivery Logistics Analysis: Binomial distribution modeling to evaluate the probability of on-time deliveries and calculate process risks.
- Online Store Order Intensity: Exponential distribution analysis to determine wait times between customer orders and survival functions.
- Hypothesis Testing: A two-tailed t-test analysis to evaluate the impact of new educational methods on student performance.
- Automated calculation of statistical characteristics (Mean, Std Dev, Median).
- Visualizations of probability densities and cumulative distributions.
- Practical implementation of hypothesis testing with p-value and confidence interval interpretation.