Week 13 / Module 12 — Multiple Regression

An outline of the module's learning topics, covering the transition from simple to multiple regression, model structure and variable selection, and interpretation of partial regression coefficients, used to guide course design and learner navigation.

A landing page for Module 12 that provides links to the PowerPoint slides, lecture recording, and a Google Colab notebook for multiple regression analysis, used by learners to access all core instructional resources in one place.

A comprehensive study reference page explaining multiple regression concepts including model definition, key characteristics, coefficient interpretation, and model evaluation, used by learners to read, review, and reinforce lecture material.

This reading explains why simple linear regression is insufficient for real-world engineering and scientific problems and introduces multiple regression as the method for modeling outcomes that depend on more than one predictor variable; learners read it to build conceptual motivation before studying the technique formally.

This reading covers how a multiple regression model is mathematically structured, including how predictors are combined with coefficients and an intercept to form the model equation; learners use it to understand how to correctly specify a multiple regression model before fitting it to data.

This reading explains how to interpret regression coefficients in a multiple regression context, emphasizing that each coefficient represents the effect of one predictor while holding all others constant; learners use it to correctly draw conclusions from estimated model parameters.

This reading covers the metrics and methods used to evaluate how well a multiple regression model fits the data, including R-squared, adjusted R-squared, and overall model significance tests; learners use it to judge whether a fitted model adequately explains variation in the outcome variable.

This reading provides a practical, end-to-end walkthrough of constructing and evaluating multiple regression models, covering predictor selection, assumption checking, and interpretation of results; learners use it as an applied guide for carrying out a full multiple regression analysis.

A 30-question quiz bank covering simple and multiple linear regression concepts such as R-squared interpretation, regression coefficients, model assumptions, and multicollinearity, used by instructors to assess learner understanding of regression topics.

A graded discussion assignment asking learners to propose a real-world example from a field of their choice where multiple linear regression with at least two independent variables would be appropriate, and to comment on a peer's response.

A hands-on assignment requiring learners to extend their Assignment 11 simple regression model by adding at least one additional predictor variable to build a two-variable multiple regression model that improves predictive performance.