STAT 324/425 -- updated as we go along the course List of Topics I. Introduction and Review Introduction, Some data sets and Splus One Sample Problems normal theory, EDA, nonnormality and dependence Two Sample Problems II. Basics in Regression Models General Model Definition and Examples Model Fitting: least squares principle and projection Interpretation of Regression Coefficients Properties: MLE, Gauss Markov theorem and extensions Index of Fit, Confidence regions and Prediction III. Model Selection, Interpretation, Correction and Extension A Case Study: Automobile data A Sequence of Models: F-tests, t-tests, ANOVA tables, Mallows Cp Variable selections: FA, BE and SW methods Interaction Discrete, categorical and continuous variables -- general and special models Added variable plots and partial residual plots Warnings, orthogonality, collinearity and identifiability Interpretation, Missing Values, ANOVA and ACOVA Generalized LS/Weighed LS Testing lack of the fit Residuals and influence Detections Corrections and Transformations Principle components and Rescaling IV. Others Robust Regression Logistic Regression - an example of GLM for categorical response variable Nonlinear Regression and Smoothing -- Brief Introduction Expriemental Designs -- Brief Introduction Biases and Bad data