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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.

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

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.

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

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Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videosTotal 13 minutes

Beyond Accuracy: The Danger of a "Wrong" Model7 minutes
Building and Diagnosing a Regression Model in R7 minutes

2 readingsTotal 11 minutes

The Anatomy of a Multiple Regression Model8 minutes
Connecting Your Skills to Your Career3 minutes

2 assignmentsTotal 35 minutes

Hands-On Learning: Build and Diagnose a Predictive Regression Model30 minutes
Knowledge Check: Interpreting Model Output and Diagnostics5 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.