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https://github.com/pegah-ardehkhani/statistics-and-probability-in-python

Explore "Statistics" and "Probability Theory" Concepts and Their Implementations in "Python"
https://github.com/pegah-ardehkhani/statistics-and-probability-in-python

confidence-intervals continuous-distributions discrete-distributions hypothesis-testing inferential-statistics matplotlib p-value parametric-hypothesis-tests probability probability-theory python python-statistics random-variables regression scipy statiscal-learning statistical-hypothesis-testing statistics statistics-for-data-science statistics-using-python

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Explore "Statistics" and "Probability Theory" Concepts and Their Implementations in "Python"

Host: GitHub
URL: https://github.com/pegah-ardehkhani/statistics-and-probability-in-python
Owner: Pegah-Ardehkhani
License: gpl-3.0
Created: 2022-06-14T21:16:01.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-07-12T15:14:10.000Z (4 months ago)
Last Synced: 2024-10-14T01:21:04.771Z (23 days ago)
Topics: confidence-intervals, continuous-distributions, discrete-distributions, hypothesis-testing, inferential-statistics, matplotlib, p-value, parametric-hypothesis-tests, probability, probability-theory, python, python-statistics, random-variables, regression, scipy, statiscal-learning, statistical-hypothesis-testing, statistics, statistics-for-data-science, statistics-using-python
Language: Jupyter Notebook
Homepage:
Size: 6.65 MB
Stars: 136
Watchers: 4
Forks: 37
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# Statistics and Probability in Python 📊 📈 ![license](https://img.shields.io/github/license/Pegah-Ardehkhani/Statistics-and-Probability-in-Python.svg) ![releases](https://img.shields.io/github/release/Pegah-Ardehkhani/Statistics-and-Probability-in-Python.svg)

> **`Note`**: This repository is still developing.

## Table of content ✍️

**Chapter 1: Special Continuous Random Variables** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%201%20Special%20Continuous%20Random%20Variables.ipynb)

- 1.1. Normal (Gaussian) Distribution
- 1.2. Chi-square Distribution
- 1.3. T-student Distribution
- 1.4. Fisher Distribution
- 1.5. Continuous Uniform Distribution
- 1.6. Exponential Distribution
- 1.7. Gamma Distribution
- 1.8. Beta Distribution
- 1.9. Weibull Distribution
- 1.10. Cauchy Distribution
- 1.11. Laplace Distribution
- 1.12. Logistic Distribution

**Chapter 2: Special Discrete Random Variables** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%202%20Special%20Discrete%20Random%20Variables.ipynb)

- 2.1. Bernoulli Distribution
- 2.2. Binomial Distribution
- 2.3. Negative Binomial (Pascal) Distribution
- 2.4. Geometric Distribution
- 2.5. Poisson Distribution
- 2.6. Discrete Uniform Distribution
- 2.7. Hypergeometric Distribution

**Chapter 3: Confidence Intervals** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%203%20Confidence%20Intervals.ipynb)

- 3.1. Confidence Interval for the Mean of a Normal Population
- 3.1.1. Known Standard Deviation
- 3.1.2. Unknown Standard Deviation
- 3.2. Confidence Interval for the Variance of a Normal Population
- 3.2.1. Unknown Mean of the Population
- 3.2.2. Known Mean of the Population
- 3.3. Confidence Interval for the Difference in Means of Two Normal Population
- 3.3.1. Known Variances
- 3.3.2. Unknown but Equal Variances
- 3.4. Confidence Interval for the Ratio of Variances of Two Normal Populations
- 3.5. Confidence Interval for the Mean of a Bernoulli Random Variable

**Chapter 4: Parametric Hypothesis Testing** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%204%20Parametric%20Hypothesis%20Testing.ipynb)

- 4.1. Introduction
- 4.2. Test Concerning the Mean of a Normal Population
- 4.2.1. Known Standard Deviation
- 4.2.2. Unknown Standard Deviation
- 4.3. Test Concerning the Equality of Means of Two Normal Populations
- 4.3.1. Known Variances
- 4.3.2. Unknown but Equal Variances
- 4.4. Paired t-test
- 4.5. Test Concerning the Variance of a Normal Population
- 4.6. Test Concerning the Equality of Variances of Two Normal Populations
- 4.7. Test Concerning P in Bernoulli Populations
- 4.8. Test Concerning the Equality of P in Two Bernoulli Populations

**Chapter 5: Statistical Hypothesis Testing** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%205%20Statistical%20Hypothesis%20Testing.ipynb)

- 5.1. Normality Tests
- 5.1.1. Shapiro-Wilk Test
- 5.1.2. D’Agostino’s Test
- 5.1.3. Anderson-Darling Test
- 5.2. Correlation Tests
- 5.2.1. Pearson’s Correlation Coefficient
- 5.2.2. Spearman’s Rank Correlation
- 5.2.3. Kendall’s Rank Correlation
- 5.2.4. Chi-Squared Test
- 5.3. Stationary Tests
- 5.3.1. Augmented Dickey-Fuller Unit Root Test
- 5.3.2. Kwiatkowski-Phillips-Schmidt-Shin Test
- 5.4. Other Tests
- 5.4.1. Mann-Whitney U-Test
- 5.4.2. Wilcoxon Signed-Rank Test
- 5.4.3. Kruskal-Wallis H Test
- 5.4.4. Friedman Test

**Chapter 6: Regression** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%206%20Regression.ipynb)

- 6.1. Introduction
- 6.2. Least Squares Estimators of the Regression Parameters
- 6.3. Statistical Inferences about the Regression Parameters
- 6.3.1. Inferences Concerning B
- 6.3.1.1. Known Variance
- 6.3.1.2. Unknown Variance
- 6.3.2. Inferences Concerning A
- 6.3.2.1. Unknown Variance
- 6.3.3. T-tests for Regression Parameters with statsmodels
- 6.3.4. F-statistic for Overall Significance in Regression
- 6.4. Confidence Intervals Concerning Regression Models
- 6.4.1. Confidence Interval for B
- 6.4.1.1. Known Variance
- 6.4.1.2. Unknown Variance
- 6.4.2. Confidence Interval for A
- 6.4.2.1. Unknown Variance
- 6.4.3. Confidence Interval for A+Bx
- 6.4.3.1. Unknown Variance
- 6.4.4. Prediction Interval of a Future Response
- 6.5. Residuals
- 6.5.1. Regression Diagnostic
- 6.5.2. Multicolinearity

**Chapter 7: Analysis of Variance (ANOVA)** [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](http://nbviewer.org/github/Pegah-Ardehkhani/Statistics-and-Probability-in-Python/blob/main/Chapter%207%20Analysis%20of%20Variance%20%28Anova%29.ipynb)

- 7.1. One-Way Analysis of Variance
- 7.1.1. Equal Sample Sizes
- 7.1.2. Unequal Sample Sizes
- 7.2. Two-Way Analysis of Variance