Predict and Validate Regression Models in R

This course is part of Advanced Survey Design & Statistical Analysis Specialization

Instructor: LearningMate

Access provided by Lockheed Martin

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.

Details to know

Shareable certificate

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Assessments

4 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Advanced Survey Design & Statistical Analysis Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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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.

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.