Introduces the simple linear regression model and the OLS criterion for fitting a line that minimizes the sum of squared residuals. Students interpret the slope and intercept in business terms, distinguish association from causation, and compute predictions and residuals while learning why extrapolation beyond the data range is unreliable.

Covers the sampling distribution of the OLS slope and its standard error, then the t-test and confidence interval for the population slope. Students distinguish a confidence interval for the mean response from a prediction interval for an individual observation and learn when each is the right tool for a business decision.

Introduces R-squared as the proportion of variance explained by a regression model, then residual plots and Q-Q plots for checking the linearity, homoscedasticity, and normality assumptions behind regression inference. Students distinguish outliers from high-leverage and influential points using Cook's distance and learn appropriate remedies for each assumption violation.

Extends simple linear regression to multiple predictors, introducing adjusted R-squared for comparing models fairly and the overall F-test for model significance. Students interpret partial slopes holding other predictors constant, encode categorical predictors as dummy variables, and diagnose multicollinearity using the Variance Inflation Factor.