IB Computer Science Topic A4.3.1: Linear Regression in Predicting Continuous Outcomes Key Definitions

A measure of model fit, written $r^2$, giving the share of the variation in the dependent variable that the model accounts for. It runs from 0, no better than always predicting the mean, to 1, a line passing through every observed point.

The predictor in a model: an input variable whose value is already known and supplied to the model so it can produce a prediction. A regression model may take several independent variables at once.

A statistical method used to model the relationship between a dependent variable (also known as the response variable) and one or more independent variables (also known as predictor variables).

The difference between an observed value and the value the regression line predicts for the same input. Linear regression chooses the line that makes the sum of the squared residuals as small as possible.