Introduction to Probability, Statistical Inference, and Regression

Data-driven decision-making relies on understanding the laws of probability and statistical relationships. This comprehensive text-based course guides you from foundational math concepts to building and interpreting your own statistical models. You will develop a strong mathematical intuition for data, learning how to formulate hypotheses, analyze experimental results, and make predictions using regression analysis. Through clear written explanations and step-by-step calculations, you will gain the confidence to analyze real-world datasets. What you'll learn: Understand core probability theory, random variables, and probability distributions; Apply statistical inference techniques, including confidence intervals and hypothesis testing; Build and interpret linear regression models to find relationships between variables; Evaluate model fit using modern diagnostic tools and residual analysis; Practice data simulation techniques to visualize probability concepts programmatically. Starting with basic probability definitions, the course progresses logically through sampling distributions and inference, concluding with practical regression modeling. Designed for beginners, this course requires no prior background in advanced statistics or calculus. Start reading today to master the essential mathematical foundations of data science.