So far in this module you have worked with regression in a fairly controlled setting: a clean dataset, a clear predictor, and a tidy output table. Real engineering data rarely arrives that way. This topic walks through the full applied workflow — choosing and cleaning a dataset, deciding which variables matter, framing a question the data can actually answer, fitting the model, reading the output in the context of the problem, checking whether the assumptions hold, refining the specification, and finally explaining what you found to someone who does not read regression tables.
This matters because almost every engineering role that uses statistics uses it this way. A coefficient only becomes useful when you can say what a one-unit change means in physical or financial terms, and a model only becomes trustworthy when you have looked at the residuals and admitted where it falls short. The judgment calls — which predictors to keep, whether an R-squared of 0.62 is good enough, whether an outlier is an error or a real signal — are the part that cannot be memorized.
As you watch, pay attention to the decisions being made rather than just the commands being typed. Notice how each presenter moves from data to a fitted model to a statement about the world, what evidence they use to justify keeping or dropping a variable, and how they describe uncertainty. Try to follow along in MATLAB, Python, or whatever tool your section uses, and ask yourself at each step how you would explain that step to a project manager.
A short, practical walkthrough from MathWorks showing how to carry a linear regression problem through a typical workflow in MATLAB — loading data, fitting a model, and examining the results the software returns. The video is oriented around the mechanics of the tool rather than the underlying derivations, so expect to see the commands and output objects you would actually use on an assignment or project.
It comes directly from the producer of the software many engineering students use, so the syntax and workflow shown are authoritative and current, and it maps cleanly onto the "building the regression model on real data" and "interpreting regression output" subtopics.
Brandon Foltz works through the decision logic of multiple regression model building: when to keep a predictor in the model and when to remove it, and how that choice changes the fit and the interpretation. The emphasis is on the reasoning behind each step rather than on a single software package.
This is the clearest match for the "refining and improving the model" and "identifying variables" subtopics, and Foltz's Statistics 101 series is known for slow, explicit explanations of why a decision is made, which is exactly what students struggle with when moving beyond a single predictor.
A hands-on Python tutorial from Coursera framed around a concrete applied question — predicting sales from advertising spend — that takes you from a real dataset to a fitted linear model. Because it is built around an actual business scenario, it also illustrates how a regression result gets stated as a practical conclusion.
The applied framing supports the "formulating a research question" and "communicating findings" subtopics, and seeing the same ideas in a second language reinforces that regression is a method, not a software feature.
Together these three videos take the mechanics you learned earlier in the module and place them in an applied workflow. The MATLAB walkthrough reinforces constructing and fitting a least-squares model and reading its output (MO1, MO2), while the Coursera tutorial shows the same process in Python anchored to a real predictive question, which supports applying regression to a real dataset and stating the result as an actionable insight (MO1, MO5). Foltz's model-building video addresses the move from one predictor to several and the judgment involved in keeping or excluding variables, directly supporting the distinction between simple and multiple regression and the interpretation of significance measures (MO2, MO3). For MO4, use the videos as a starting point but rely on your course materials and lab work for the formal residual and assumption checks, since none of these short videos is primarily a diagnostics tutorial.
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.