HW Assignment 1 Due Thurs., Jan. 22.
HW 1 solution,
Problem 5 program, Problem 5 program output,
Problem 7 program, Problem 7 program output.
Lecture 2 (Tues., Jan. 13): Matrix review, multivariate normal review.
Lecture 3 (Thurs., Jan. 15): Start Ch. 4, models for longitudinal data.
Tuesday, January 20. Class cancelled due to snow.
Lecture 4 (Thurs., Jan. 22): Split plot model in sas, other notation for longitudinal models, hypotheses in terms of CMU.
HW Assignment 2 Due Thurs., Feb. 5.
HW 2 solution,
Problem 4 program, Problem 4 program output,
Problem 6 program, Problem 6 program output.
Lecture 5 (Tues., Jan. 27): SAS glm with the repeated statement.
Lecture 6 (Thurs., Jan. 29): Multivariate statistics via glm and the repeated statement and manova options (Ch. 6), restrictions of glm (Ch. 7).
Lecture 7 (Tues., Feb. 3): Introduction to modeling longitudinal data in proc mixed, covariance structure selection.
Lecture 8 (Thurs., Feb. 5): "Noint" parameterization and "Difference" parameterization for dental data. Analysis for the dialyzer data, where pressure is measured at different values.
HW Assignment 3 Due Thurs., Feb. 19. HW 3 solution, Problem 3 program, Problem 3 program output.
Lecture 9 (Tues., Feb. 10): Analysis of the hip replacement data, with missing response values, proc mixed parameters--contrast and estimate statements.
Lecture 10 (Thurs., Feb. 12): Units within blocks over time and comparisons with standard ANOVA.
Lecture 11 (Tues., Feb. 17): Begin Ch. 9, random coefficient models.
Lecture 12 (Thurs., Feb. 19): Questions about Mid-term. Random cofficient models, dental data and dializer data.
Mid-term Exam (Tuesday, Feb. 24). Solutions.
Lecture 13 (Thurs., Feb. 26): Ch. 10. The general linear mixed model: linear terms that are either fixed or random, Best Linear Unbiased Prediction (BLUP) of random quantities.
Spring Break, March 2-6.
Lecture 14 (Tues., March 10): More on BLUPS and EBLUPS. Weight lifting example.
HW Assignment 4 Due Thurs., March 19. HW 4 solution, Problem 3 program, Problem 3 program output.
Lecture 15 (Thurs., Mar. 12): Testing of variance components.
Tuesday, March 17 - No class! ENAR Meetings.
Lecture 16 (Thurs., Mar. 19): Ch. 11: generalized linear models, independent Y's. Poisson example.
Lecture 17 (Tues., Mar. 24): Ch. 11: glm's continued. Score equations and asymptotic variance. Binary and gamma examples.
Lecture 18 (Thurs., Mar. 26): Ch. 12: begin GEE, extension of glm's to longitudinal data.
HW Assignment 5Due Thurs., April 9. HW 5 solution, Problem 1 program, Problem 1 output. Problems 2 and 3 program, Problems 2 and 3 output. Problem 4 program, Problem 4 output.
Lecture 19 (Tues., March 31): Ch. 12, more GEE.
Lecture 20 (Thurs., April 2): Ch. 12: GEE, sandwich matrix details.
Lecture 21 (Tues., April 7): Student essays consulting example, proc mixed.
Lecture 22 (Thurs., April 9): Student essays consulting example, proc genmod, cumulative logit.
Lecture 23 (Tues., April 14): Random coefficient models in binary logistic regression, schizophrenia example.
Lecture 24 (Thurs., April 16): Random coefficient models in ordinal logistic regression, schizophrenia example.
Lecture 25 (Tues., April 21): Choosing longitudinal models--review.
Lecture 26 (Thurs., April 23): Project discussion.