Hypothesis Testing

In this part of my practise the following hypothesis were performed.

• Independent T-test to test the difference between two groups
• Welch's T-test for sampples with unequal variances
• Paired difference T-test for assess the significance of an intervention
• Applying one-way and two-way ANOVA for multiple categorical values

The test were performed on different datasets for the following purpose:

1. Bike sharing data set: To test whether weather situation, temperature affected the numbe of bikes rented.
2. Blood pressure dataset: To test the blood pressure results before and after the intervention are statistically significant or not
3. Women's clothing e-commerce reviews data set: To test whether the frequency distribution of a dataset matches the expected distribution

Tasks included

Following tasks related to hypothesis testing and data visualization were done:

• Hypothesis testing

• Data visualization

  • Box plot visualization
  • Histogram visualization
  • Q-Q plot
  • Barchart visualization

Datasets

You can find all the datasets used in this project inside the datasets directory. The datasets include

• Bike_sharing_dataset_preprocessed.csv
• Blood_pressure.csv
• Womens Clothin E-Commerce Reviews.csv

Installation

You can clone the repository and practise for yourself.

Credits

This is a part of Pluralsight's - Interpreting Data Using Statistical Models with Python

Thank you for your support ❤️