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Unformatted text preview: August 23, 2011 Syllabus for Stat 4510 / 7510 APPLIED STATISTICAL MODELS I Fall 2011 Instructor : Sounak Chakraborty Class time : 8.00 am - 9.15 am Tu Th Location : MIDDLEBUSH HALL 310 Office : 209F Middlebush Building Hours Available Outside of Class : Thursday 12.00 pm - 1.00 pm. Outside office hours by appointment only. E-mail : email@example.com SAS User Support : Ray Bacon Office : Middlebush Room 8 Help Session : 5.00 pm - 7.00 pm Mon - Fri (This help session is only for SAS help) E-mail : BaconR@Missouri.edu. TA : Help Session from TA : TA Office : TA E-mail : Textbook : Applied Regression Analysis and Other Multivariate Methods, Fourth Edition, by Klein- baum, Kupper, Nizam, and Muller. Topics to be Covered : Descriptive statistics: one sample inference for mean and variance, Type I and II errors, sample size and power; two-sample comparison of means and variances. Simple linear regression: Basic regression model and assumptions, least squares estimation; normal error and inference on regression coefficient, intercept, mean response, prediction; ANOVA table, residuals and regression diagnostics, measurement errors. Multiple Regression: ANOVA table, inference on regression coefficient, mean and pre- diction, partial determination, Type I and II sums square, polynomial regression, categorical variables, interaction term, diagnostic (outliers of X, Y, influential observation, multicolinear- ity), remedial measures (weighted least squares, variance stabilizing transformations), model selection and validation. Analysis of Designed Experiments / One Way ANOVA : Completely randomized designs, one-factor fixed effects model, alternative formulations and restrictions on parameters, contrasts, multiple comparisons; estimation and inference, F- and t-tests. Multi-Factor ANOVA: Two-factor fixed effects models (balanced and unbalanced designs, alternative models formulations and restrictions, interaction, orthogonal contrasts, non-orthogonal decompositions, Type III sums of squares, F-tests. Introduction to Random Effects: One-factor random effects models, estimation, F-tests; two-factor random/mixed effects models (balanced designs, variance components, estimation and inference)....
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