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Predict and Validate Regression Models in R

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Predict and Validate Regression Models in R

This course is part of multiple programs.

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Beginner level

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2 hours to complete
Flexible schedule
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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build and validate linear regression models in R, using diagnostics and cross-validation to ensure robust, reliable business predictions.

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Recently updated!

March 2026

Assessments

4 assignments¹

AI Graded see disclaimer
Taught in English

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There are 2 modules in this course

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.

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.

What's included

2 videos2 readings2 assignments

2 videos•Total 13 minutes
  • Beyond Accuracy: The Danger of a "Wrong" Model•7 minutes
  • Building and Diagnosing a Regression Model in R•7 minutes
2 readings•Total 11 minutes
  • The Anatomy of a Multiple Regression Model•8 minutes
  • Connecting Your Skills to Your Career•3 minutes
2 assignments•Total 35 minutes
  • Hands-On Learning: Build and Diagnose a Predictive Regression Model•30 minutes
  • Knowledge Check: Interpreting Model Output and Diagnostics•5 minutes

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.

What's included

2 videos2 readings2 assignments

2 videos•Total 13 minutes
  • The High-Stakes World of Clinical Trials•7 minutes
  • Implementing 10-Fold Cross-Validation in R•6 minutes
2 readings•Total 13 minutes
  • Understanding K-Fold Cross-Validation•8 minutes
  • Your Future in Advanced Analytics•5 minutes
2 assignments•Total 60 minutes
  • Predict and Validate Housing Prices•30 minutes
  • Hands-On Learning: Validate Model Stability with K-Fold Cross-Validation•30 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.