Module Overview
An outline of the module's topics covering the foundations of regression analysis, simple linear regression (including the least squares method), and multiple regression models, used by instructors to understand the scope and structure of the module.
A hub page linking learners to the Module 11 PowerPoint slides, lecture video, and a Google Colab notebook for hands-on regression analysis practice.
A study reference page explaining key regression analysis concepts—including simple linear regression, model definition, and interpretation—supported by links to the module lecture, used by learners to review and reinforce core content.
Topic Readings
This reading introduces regression analysis as a statistical method for modeling relationships between variables, covering its core concepts, purposes, and applications across fields; learners use it to build conceptual understanding before diving into specific regression techniques.
This reading explains simple linear regression, covering the mathematical relationship between one predictor and one outcome variable, how the regression line is fitted, and interpretation of slope and intercept; learners use it as the foundational reference for understanding and applying single-predictor regression models.
This reading extends regression to models with two or more predictor variables, covering how multiple predictors are incorporated simultaneously, coefficient interpretation, and the motivation for using multiple regression over simple linear regression; learners use it to understand and build more realistic predictive models.
This reading covers the metrics and diagnostic tools used to assess how well a regression model fits data, including R-squared, residual analysis, and other performance indicators; learners and instructors use it to evaluate and compare regression models after fitting them.
This reading explains how to read and interpret regression output tables, covering coefficients, p-values, confidence intervals, and common pitfalls in interpretation; learners use it as a practical guide to translate regression results into meaningful, real-world conclusions.
This reading addresses the application of regression analysis to messy, real-world datasets, covering challenges such as missing data, skewed distributions, and multicollinearity, along with strategies to handle them; learners use it to bridge the gap between textbook methods and practical data analysis workflows.
Assessment & Assignments
A 35-question quiz bank assessing learners on regression analysis concepts such as the least squares method, homoscedasticity, R-squared interpretation, multicollinearity, and coefficient interpretation in simple linear regression models.
A graded discussion assignment asking learners to provide a real-world example from a field of their choice where linear regression would be a good fit, then review and comment on at least one classmate's response.
A graded assignment in which learners download a Kaggle housing dataset and build a regression model to predict house price as a function of square footage of living area.