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Paul Gustafson
Course Outline (REVISED VERSION OF SEPT. 6, pdf file) Assigned coursework (will be added onto as we cover material). Page last updated: Nov. 22, 2005 Lecture 1: Statistical Principles I: Estimation (handwritten notes). Lecture 2: Statistical Principles II: Uncertainty assessment and hypothesis testing (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 3: Nice things about R (slides  pdf: fullsize, reduced, ps: fullsize, reduced ), and three example tasks (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 4: Linear models, Part I (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 5: Linear models, Part II: handwritten slides on regression diagnostics. Lecture 6: Logistic regression (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 7: Generalized linear models I (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 8: Generalized linear models II  Overdispersion (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 9: Generalized linear models III  Loglinear modelling (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). We lapped ourselves. It took us 10 classes to cover lectures 1 through 9. Hence there is no lecture 10. Lecture 11: The bootstrap (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Also, here is R code for examples one, two, and three. Lecture 12: Model Choice (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 13: More Model Choice (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Also, R code for the stepwise and crossvalidation examples. Lapped ourselves again  4 classes to cover lectures 11 through 13  Hence there is no lecture 14. Lecture 15: Nonlinear Regression (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 16: ExpectationMaximization (EM) Algorithm (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 17: Simulation Studies (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 18: Handwritten notes on hierarchical models (otherwise known as randomeffect models, mixed models, or random coefficient models). Lecture 19: More on hierarchical models, including the Sitka data ex.: (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 20: Curvefitting. No prefab slides, but some pictures ( pdf, or ps), and a bit of code. Lecture 21: Move from curvefitting to additive models, with this example: (slides  pdf: fullsize, reduced, ps: fullsize, reduced ). Lecture 22: We didn't really get started on additive models last time, so that discussion carrys over. We may also start talking about "tree models" if time permits. Lecture 23: Tree models. Here are the examples. ( pdf: fullsize, reduced, ps: fullsize, reduced ).


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