# 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** Open In Colab [![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 **Chapter 2: Special Discrete Random Variables** Open In Colab [![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** Open In Colab [![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** Open In Colab [![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** Open In Colab [![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** Open In Colab [![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)** Open In Colab [![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