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       	
        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  
        Corrections and Transformations
        Principle components and Rescaling 
IV.   Others
        Robust Regression

        Logistic Regression - an example of GLM for categorical response

        Nonlinear Regression and  Smoothing -- Brief Introduction

        Expriemental Designs  -- Brief Introduction    
        Biases and Bad data