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Scatterplots and regression. 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. Simple linear regression as a special case. 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. Polynomials and factors in regression. Weighted least squares.