AP®︎ Statistics (pre-2026): Topic 2.9 Flashcards

A data point whose removal substantially changes the regression line or correlation.

A point far from the overall pattern vertically, with a large residual.

A point with an x-value much farther from the others than most observations.

Outliers are unusual in y with large residuals; high-leverage points are unusual in x.

A new data set created by applying a function to x, y, or both.

Applying transformations so a curved pattern becomes approximately linear for regression.

A nonlinear model of the form y-hat = ab^x, with constant multiplicative change.

A nonlinear model of the form y-hat = ax^b, relating variables by powers.

Take ln(y) so the model becomes linear: ln(y-hat) = ln(a) + x ln(b).

Take ln(x) and ln(y) so the model becomes linear: ln(y-hat) = ln(a) + b ln(x).

A better model has residuals that look more random and less curved after transformation.

A transformed model is stronger when its r-squared moves closer to 1.

Use the transformed line, then undo the transformation to predict on the original scale.

Outliers and high-leverage points are often influential because they can strongly change regression results.