https://github.com/jacekkala/statistics_hypothesis_testing

Statistics & Hypothesis Testing in Python
https://github.com/jacekkala/statistics_hypothesis_testing

charts hypothesis-testing jupyter-notebook matplotlib numpy pandas python scipy-stats seaborn statistics

Last synced: 26 days ago
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Statistics & Hypothesis Testing in Python

• Host: GitHub
• URL: https://github.com/jacekkala/statistics_hypothesis_testing
• Owner: jacekkala
• License: mit
• Created: 2024-07-19T16:43:25.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-02T10:51:03.000Z (about 1 year ago)
• Last Synced: 2025-02-06T10:23:27.537Z (12 months ago)
• Topics: charts, hypothesis-testing, jupyter-notebook, matplotlib, numpy, pandas, python, scipy-stats, seaborn, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 3.91 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 📊 Statistics & Hypothesis Testing in Python using Jupyter Notebooks

Welcome to the **Statistics & Hypothesis Testing in Python** repository! This collection of Jupyter Notebooks offers an in-depth exploration of various statistical concepts and hypothesis testing techniques, all presented in a visually appealing and comprehensive manner.

## 📚 Notebooks Overview

Each notebook in this repository is meticulously crafted, providing:

- **Rich Descriptions**: Clear objectives, theoretical background, and conclusions for each experiment.

- **Beautiful Visualizations**: A wide range of charts and visual aids that are not only informative but also aesthetically pleasing.

## 🌟 Highlights

- **Engaging Visuals**: Each notebook is packed with charts and graphs that are not only functional but also crafted to be visually appealing.

- **Comprehensive Coverage**: From basic concepts to advanced hypothesis testing methods, this repository covers a broad spectrum of topics.

- **Educational Value**: Detailed explanations and clear objectives make it easy to understand the purpose and outcomes of each experiment.

## 📁 Repository Contents

Here's a breakdown of the notebooks included in this repository:

1. **Introduction**

   - Understanding dataset structure

   - Selecting subsets of columns

   - Constructing histograms and dotplots

2. **Sampling Methods**

   - Applying sampling methods

   - Evaluating sample representativeness

3. **Visualizing Data**

   - Creating Bar Charts, Pie Charts, Stem-And-Leaf Plots, Histograms

4. **Descriptive Statistics**

   - Determining numerical characteristics

   - Constructing boxplots

5. **Correlation**

   - Generating scatterplots

   - Computing correlation coefficients

6. **Linear Regression**

   - Performing correlation and regression analysis

7. **Multiple Linear Regression**

   - Constructing and evaluating models

   - Interpreting coefficients

   - Diagnosing multicollinearity

8. **Logistic Regression**

   - Implementing logistic regression

   - Performing classification

9. **Examining Normality**

   - Assessing normality using QQ-plots

10. **Central Limit Theorem**

    - Investigating the Central Limit Theorem

    - Using uniform and exponential distributions

11. **Properties of Probability Estimation**

    - Exploring probability estimation

12. **Bootstrap**

    - Learning the bootstrap method

13. **Student's t-Test**

    - Applying Student's t-distribution

    - Comparing sample means

14. **Single Sample Population Mean Test**

    - Testing population means

    - Assessing evidence against null hypotheses

15. **One Sample Proportion Test**

    - Conducting one-sample proportion tests

    - Analyzing model data

16. **Standard Vs Welch's t-Test**

    - Testing means for two samples

    - Comparing variances

17. **Paired t-Test**

    - Conducting paired t-tests

    - Assessing population mean differences

18. **Independent Samples Proportions Z-test**

    - Testing differences in proportions

19. **Density & Distribution Functions**

    - Comparing empirical and theoretical probability functions

20. **Chi-Squared Goodness of Fit Test**

    - Understanding chi-square goodness of fit

21. **Kolmogorov-Smirnov Test**

    - Applying Kolmogorov-Smirnov tests

22. **Tests on Normality**

    - Assessing sample data normality

    - Using Q-Q plots and various tests

23. **Relationship Between Categorical Variables**

    - Testing relationships using Chi-squared and Fisher's Exact Test

24. **Association Between Two Binary Variables**

    - Testing and measuring associations

25. **Analysis of Variance (ANOVA)**

    - Conducting one-factor ANOVA

    - Interpreting results

   

## 🔍 Explore and Learn

Dive into the notebooks to explore various statistical methods and hypothesis tests. Whether you're a beginner looking to learn the basics or an advanced user seeking to deepen your understanding, this repository has something for everyone.

Happy Learning! 🎓

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Feel free to reach out if you have any questions or suggestions. Contributions are always welcome!

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### 📬 Contact

- GitHub: [@Jankiel-Predator](https://github.com/Jankiel-Predator)