IB Math AI Worksheet AHL 4.13 - Regression Analysis

This IB Math AI HL 4.13 – Regression Analysis resource develops students’ understanding of how non-linear relationships can be modelled, assessed, and interpreted. Students explore least squares regression, residuals, sum of squared residuals (SSR), and the coefficient of determination (R²), learning how model choice affects fit, interpretation, and prediction in real contexts. The material links regression directly to variation, goodness of fit, and the limits of extrapolation.

Structured tasks guide learners in selecting appropriate non-linear models (exponential, power, logarithmic), using transformations to linearise data, comparing models with SSR, and interpreting regression output from technology. Extended and exam-style problems develop higher-level reasoning about outliers, transformation risks, and choosing realistic models for prediction. With applied contexts and a full answer key, this resource supports HL teaching, IA preparation, and deeper statistical modelling skills in line with IB Mathematics AI expectations.