Predict and Validate Regression Models in R

Overview

This course is your entry point into the world of predictive analytics with R. Designed for aspiring data analysts and business professionals, this course empowers you to build and interpret multiple linear regression models from the ground up. You will move beyond simply running code and learn to critically evaluate your model's performance. Through a series of hands-on learnings and real-world case studies, you will master the techniques to diagnose your model's statistical assumptions using residual plots and assess its reliability with k-fold cross-validation. By the end of this course, you won't just build models—you'll build models you can trust. You'll leave with a validated, portfolio-ready project and the confidence to generate dependable forecasts that drive strategic business decisions.

Syllabus

Building and Diagnosing Your First Regression Model

This module introduces the fundamentals of predictive modeling with multiple linear regression. You will learn how to formulate, build, and interpret a regression model in R to predict outcomes like housing prices or customer churn. More importantly, you will learn to look beyond surface-level accuracy by generating and analyzing key diagnostic plots to ensure your model is statistically sound and free of common pitfalls such as nonlinearity or heteroscedasticity.

Validating Model Performance and Stability

In this module, you will learn that a model is only useful if its performance is reliable. You will move beyond single-score accuracy to master k-fold cross-validation—a powerful technique for ensuring your model's stability and ensuring that it generalizes to new, unseen data. You will implement this technique in R, analyze the variance in performance across folds, and learn how to confidently report on your model's robustness, a key skill for any data professional.