IB Maths AI 4.9 Definitions

The proportion of the variation in $y$ explained by the chosen model, with values from $0$ to $1$; higher values usually indicate a better fit.

The regression curve (of a chosen model type) that minimises $SS_{\text{res}}$ for the given data set.

Using technology to fit a curved model (not of the form $y=ax+b$) to data by estimating constants so the curve matches the overall pattern of the points.

The total squared error from a model, calculated as $SS_{\text{res}}=\sum\left(y_{\text{actual}}-y_{\text{predicted}}\right)^2$, used to compare how closely different models fit the same data.

A measure of fit found by adding the squared residuals for all data points, $SS_{\text{res}}=\textstyle\frac{}{}\sum\left(y_{\text{actual}}-y_{\text{predicted}}\right)^2$; smaller values indicate a closer fit to the data.