Regression Analysis in R for Statistics and Machine Learning

Understanding the relationship between variables is the cornerstone of data science, business forecasting, and scientific research. This text-based course guides you through the fundamentals of regression analysis, bridging the gap between classical statistics and modern machine learning. You will transition from understanding basic correlation to building, diagnostic testing, and deploying robust predictive models using R. Through clear written explanations and practical R code examples, you will learn how to handle real-world data challenges like multicollinearity and non-linear relationships. By the end of this course, you will have the confidence to choose the right regression model for your data, validate its assumptions, and interpret the results with scientific accuracy. What you'll learn: - Learn the foundational concepts of simple and multiple linear regression. - Build and interpret ordinary least squares (OLS) regression models in R. - Diagnose and resolve common model issues such as multicollinearity and heteroscedasticity. - Apply regularized regression techniques including Ridge and Lasso to prevent overfitting. - Implement modern machine learning regression workflows using the tidymodels ecosystem. - Evaluate model performance using cross-validation and predictive metrics. The course starts with essential statistical definitions and basic correlation before moving systematically through linear, logistic, and advanced machine learning regression techniques. You will read through step-by-step code implementations and learn how to interpret statistical outputs for real-world application. This course is designed for beginners, aspiring data scientists, and researchers who want a solid foundation in regression analysis using R. No prior modeling experience is required, though a basic familiarity with R syntax is helpful. Start mastering regression analysis to unlock deeper insights from your data today.