This course introduces regression analysis techniques through hand-on data analysis, with focuses on the application of linear regression models in practice. The fundamentals of simple linear regression, multiple linear regression and nonlinear regression will be introduced in class, involving the use of standard statistical software. The following topics will be included: statistical review, Regression Inference, Inference on Mean Response and Prediction, ANOVA and General Linear Test, Coefficient of Determination, Residual Graphics and Diagnostics, Residual Diagnostic Tests, Lack of Fit Test, Remedial Measures, Simultaneous Inference, Regression in Matrix Form, Multiple Regression (including dummy variables), Model Selection, Influence, Leverage, and Multicollinearity, Remedial Measures Influence, Leverage, and Multicollinearity, Nonlinear (Logistic) Regression, Logistic Regression, Poisson Regression, and Generalized Linear Models.

STAT 2110 Applied Statistics is required. MATH 1220 Calculus 2 and MATH 2160 Linear Algebra is recommend.