Regression Analysis in R: A Practical Introduction

Regression analysis is the foundation of data-driven decision making and predictive modeling. This text-based course guides you through the principles of statistical modeling in R, providing clear explanations and practical code examples to help you analyze relationships in your data with confidence. You will build a solid understanding of how linear and generalized linear models work, from loading data to evaluating performance metrics and drawing actionable conclusions. What you'll learn: - Understand essential statistical terminology, variables, and regression fundamentals - Fit simple and multiple linear regression models using standard R syntax - Implement logistic regression to analyze binary outcomes and classification problems - Diagnose regression assumptions, evaluate residuals, and identify multicollinearity - Apply tidy data principles and modern packages for clean model evaluation - Interpret regression coefficients and communicate statistical findings clearly The course begins with foundational definitions and statistical concepts before advancing step by step through model fitting, assumption checks, and practical interpretation exercises. Designed specifically for beginners in data analysis and statistics, no prior modeling experience is required. Start reading today to add practical regression modeling to your analytical skillset.