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To introduce students to Linear Models.
Scatterplots and regression. Simple linear regression: estimation, parameter inference, prediction, analysis of variance, R2, F-test, correlation, residual plots, assessing normality. The multiple regression model and its applications. Matrix notation, mean vectors and covariance matrices. Least-squares estimation. The multivariate normal distribution. Hypothesis testing and confidence intervals, prediction. Analysis of variance, R2, sequential sums of squares, general F-tests. Added variable plots. Model checking via testing for lack of fit and residual plots.
Regression diagnostics: residuals, leverage, outliers, influence and Cook's Distance.