Simple linear regression models an outcome using a single predictor, and it works well when one variable really does carry most of the explanatory weight. In engineering practice, that is rarely the case: fuel consumption depends on more than speed, concrete strength depends on more than cure time, and the price of a used car depends on more than mileage. When important variables are left out, the slope you estimate for the one predictor you kept can absorb the influence of everything you ignored, and your predictions and conclusions suffer.
Multiple regression extends the same least-squares idea to two or more predictors at once. The structure is familiar — an intercept, a slope for each predictor, and residuals — but the interpretation changes in an important way: each coefficient now describes the effect of its own predictor while the other predictors are held constant. That idea, often called controlling for other variables, is the conceptual bridge between what you learned last week and what you will do for the rest of this module.
As you watch these videos, focus on three things: how the simple regression equation is generalized to several predictors, what the phrase "holding the other variables constant" actually means for a fitted coefficient, and what signals tell you that one predictor is not enough. Keep notes on the vocabulary (predictor, response, coefficient, controlling for), because the later topics on R-squared, adjusted R-squared, and the F-test all build directly on it.
A compact overview of multiple linear regression built around a worked illustration: predicting the price of a used car from several predictor variables using Excel. Based on its structure and timestamps, it introduces what multiple regression is, how the model is set up with more than one predictor, and how output is produced and read from a spreadsheet tool.
Dr Nic's Maths and Stats is a well-regarded statistics education channel, and the used-car example makes the motivation for adding predictors concrete, which maps directly onto the "What Is Multiple Regression?" and "Why Add More Predictor Variables?" subtopics.
A short animated explainer from CLOSER on regression analysis as a general tool for examining relationships between variables. The description indicates it covers relationships between two or more variables, so expect it to move from the single-predictor case toward the idea of modelling an outcome with multiple explanatory variables.
Its brevity and animation make it a good conceptual primer on the continuity from simple to multiple regression, useful before you tackle the algebra and software output.
A follow-on video focused specifically on how to read regression coefficients, in both the single-predictor and multiple-predictor settings. It is positioned as a continuation of the overview video, so it should address what each estimated slope means once other predictors are present in the model.
Coefficient interpretation is exactly where simple and multiple regression diverge, making this the clearest fit for the "Controlling for Other Variables" subtopic and the best preparation for partial coefficients later in the module.
Together these videos set up the conceptual foundation the module's outcomes rest on. The overview and the animated explainer show how a model with several predictors is constructed and why additional variables are worth including, which supports MO1 and informs the judgment calls behind MO5. The coefficients video speaks directly to MO2, since interpreting a slope as a unique effect while other predictors are held constant is the central skill there. These are introductory and conceptual treatments, so the detailed machinery for MO3 (R-squared versus adjusted R-squared) and MO4 (the overall F-test) is developed in the lectures, readings, and later topics of this module — use the videos to build intuition first, then return to the formal criteria.
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.