Fitting a regression model is only the first step; deciding whether that model can be trusted is the real engineering work. A model can have a high R-squared and still be built on unstable coefficients, be driven by a handful of unusual data points, or hide predictors that duplicate each other's information. This topic gives you a systematic way to look for those weaknesses before you report a result or use a model to make a design or process decision.
Three problems come up again and again. Multicollinearity occurs when predictor variables are strongly related to one another, which inflates standard errors and makes individual coefficient estimates unstable and hard to interpret; the variance inflation factor (VIF) is the standard numerical check. Outliers are observations with unusually large residuals — they sit far from the fitted line. Influential observations are points whose removal would noticeably change the fitted model, and these are usually points with high leverage (an unusual combination of predictor values) combined with a sizable residual. Outlier and influential are not the same thing, and keeping that distinction straight is one of the main goals of this week.
As you watch, build yourself a checklist: what quantity is being computed, what plot displays it, and what cutoff or rule of thumb signals a problem. Pay attention to how leverage, residuals, and influence relate to each other, and think about what you would actually do next — drop a variable, transform it, re-examine the data point, or collect more data. Also notice the consequences of ignoring these issues: misleading p-values, coefficients with the wrong sign, and predictions that fail on new data.
This lecture segment introduces collinearity among predictors and the variance inflation factor (VIF) as the standard diagnostic for it. Based on the title and description, it begins with exact (perfect) collinearity and a formal definition, then develops VIF and its connection to the R-squared obtained by regressing one predictor on the others. Expect the discussion to explain why correlated predictors inflate the variance of estimated coefficients.
It is a focused, university-lecture treatment of the multicollinearity subtopic, and tying VIF back to an auxiliary R-squared gives you the intuition behind the number rather than just a threshold to memorize.
A short lecture clip that separates two ideas students often merge: an outlier (a point with a large residual) and an influential observation (a point that actually moves the fitted line). The description indicates it works through when an observation has high leverage and when that leverage translates into influence on the regression fit. Expect conceptual explanation with reference to how the fitted line is constructed.
It directly addresses the "Detecting Outliers" and "Identifying Influential Observations" subtopics and clarifies a distinction that is essential before you can interpret diagnostic plots correctly.
This longer diagnostics segment centers on leverage and how it combines with residual size to change the estimated regression slope. The description points to the key case: a high-leverage point paired with a large residual produces a substantial shift in the fit. Expect coverage of leverage as a measurable quantity and its role in a broader set of regression diagnostics.
At about eleven minutes it gives the most detailed treatment of leverage and influence here, supporting both the diagnostic-plot interpretation subtopic and the consequences of leaving such points unexamined.
Together these videos target MO4 most directly: the VIF lecture supplies the numerical tool for diagnosing multicollinearity, while the outlier-versus-influence clip and the leverage diagnostics lecture supply the concepts and measures needed to flag unusual and influential observations. Because leverage and influence are read off residual-based diagnostic plots, the latter two also reinforce MO1 and MO2 by connecting residual patterns to specific model problems. Finally, knowing which weakness you have — a redundant predictor versus a single distorting observation — is what lets you choose an appropriate remedy such as variable removal or transformation, supporting MO5.
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.