Statistics Lectures

Learn the fundamentals of statistical analysis and data interpretation. This comprehensive lecture covers basic concepts, terminology, and practical applications in statistical thinking.

Master linear and multiple regression techniques for data modeling. Learn to build, interpret, and validate regression models for real-world applications.

Understand statistical inference and hypothesis testing methods. Learn to design experiments, analyze results, and make data-driven decisions.

Explore Bayesian approaches to statistical analysis and modeling. Understand prior distributions, posterior inference, and MCMC sampling techniques.

Introduction to supervised and unsupervised learning algorithms. Learn about classification, regression, clustering, and model evaluation techniques.

Create compelling visualizations using ggplot2 and other R packages. Learn best practices for effective data communication and visual storytelling.