Week 14 / Module 13 — Validating Regression

A structured outline of Module 13 topics covering regression validation foundations, model assumptions (linearity, homoscedasticity, independence), residual analysis, and cross-validation techniques, used to guide the organization and sequencing of the module's instruction.

A module landing page that links learners to the Module 13 lecture presentation, supporting video lecture, and a Google Colab notebook for hands-on regression validation exercises.

A comprehensive reading that defines and explains residual analysis, key regression model validation concepts, and supporting techniques, used by learners as a primary reference while working through the module.

This reading introduces regression validation as a structured process for assessing whether a regression model is genuinely trustworthy beyond surface-level metrics like R-squared, and learners use it to understand why and how model checking is necessary before drawing conclusions.

This reading explains the core assumptions underlying ordinary least squares linear regression (such as linearity, independence, homoscedasticity, and normality of errors) and how learners and instructors can identify when those assumptions are met or violated.

This reading covers residual analysis as a diagnostic tool for evaluating whether a fitted regression model is correct, teaching learners how to examine patterns in residuals to detect assumption violations and model misspecification.

This reading explains cross-validation techniques—including holdout sets and k-fold cross-validation—and how learners use them to assess a model's ability to generalize to new data rather than just fitting the training sample.

This reading covers three major categories of structural regression model weaknesses—such as multicollinearity, influential observations, and heteroscedasticity—and teaches learners how to identify and diagnose these problems before they corrupt model estimates.

This reading covers the process of iteratively refining a regression model for trustworthiness and generalizability, guiding learners and instructors through balancing statistical fit with practical performance to support sound decision-making.

A 35-question quiz bank covering residual plot interpretation, Cook's Distance, variable transformations, k-fold cross-validation, and prediction intervals, used by instructors to assess learners' understanding of regression validation concepts.

A graded discussion prompt asking learners to evaluate and comment on methods for detecting dependency between independent and dependent variables, used to encourage reflection on regression validation approaches across different data types.

A hands-on assignment that extends the Module 12 multiple regression report by adding model fit validation using a systematic approach (visualization through statistics), giving learners practice applying validation techniques to their own dataset.