Welcome to the Probability And Statistics Practice Code repository! This repository contains code examples and solutions for practicing probability and statistics concepts. Whether you're a student learning these topics for the first time or a professional looking to sharpen your skills, you'll find a variety of examples and exercises to reinforce your understanding.
This repository is a collection of code examples and exercises covering various topics in probability and statistics. Each topic is organized into separate directories, containing code snippets, Jupyter notebooks, and datasets to help you practice and understand key concepts.
- Basic Probability: Introduction to probability theory, including events, sample spaces, and probability rules.
- Basic Statistics: Fundamental statistical concepts such as measures of central tendency and dispersion.
- Descriptive Statistics: Calculation and visualization of descriptive statistics like mean, median, mode, variance, and standard deviation.
- Inferential Statistics: Sampling distributions, confidence intervals, and hypothesis testing.
- Confidence Interval: Calculating confidence intervals for population parameters based on sample data.
- Hypothesis Test: One-sample, two-sample, and proportion tests for hypothesis testing.
- Chi-Square Test: Tests of independence and goodness of fit using the chi-square statistic.
- ANOVA Test: Analysis of variance for comparing means across multiple groups.
- Covariance and Correlation: Calculation and interpretation of covariance and correlation coefficients.
- Regression Analysis: Simple and multiple linear regression, logistic regression, and model evaluation techniques.
To get started, clone this repository to your local machine:
pip install numpy pandas matplotlib seaborn jupyter
git clone https://github.com/your-username/probability-statistics-practice.git