Topics covered in STA 442/2101 Fall Term 2009 

Linear regression (Ex G, venice sea level): fitting, transformation of variables, interaction, residuals, influence, model selection, analysis of variance, multicollinearity, model choice via AIC and stepwise selection.

Sections 8.1, 8.2, 8.3, 8.5, 8.6, 8.7

Balanced linear model (ExJ, seedrate, teaching methods): factorial designs, quantitative and qualitative factors, interaction, orthogonal polynomials, successive least squares fits, orthogonal contrasts, partitioning sums of squares, analysis of covariance, random effects models and components of variance.

Sections 9.2.1, 9.2.2, 9.2.4, 9.3.1, 9.3.2, 9.4

Logistic regression (ExH, Example 10.18 nodal involvement, Challenger explosion, Berkeley admissions): likelihood fitting, maximum likelihood estimates, likelihood ratio tests, matched pairs designs. Generalized linear models, Poisson regression, overdispersion.

Sections 10.3, 10.4.1, 10.6

Survival data (Oscar winners): hazard function, proportional hazards model, Kaplan-Meier estimator, time-dependent covariates 

Sections 10.8.2, 5.4.1, 5.4.3

In the News:  Beautiful daughters -- statistical significance, multiple comparisons;
Spanking and IQ -- correlation and causation; Berkeley admissions and capital punishment -- Simpson's paradox; Oscar winners live longer -- survivor treatment selection bias

Theory:  linear models, least squares and weighted least squares, maximum likelihood estimation, models for survival data, nonparametric estimation of survivor functions, partial likelihood for proportional hazards regression