Home page for EEB 596Z:
Issues in Biostatistical Analysis

 You are visitor number   since 13 August 1999 

Lecture schedule --- Info on students ---- Homework --- a few statistics links (under construction) -- selected references (under construction)

Course information

This course is designed as a lecture course covering various topics in Statistical analysis (see below). I assume students have some modest background in statistics and we build on this by discussing a number of topics. The goal of this course is to provide students with a better feel for statistics and to be much less intimidated by methods of statistical analysis.

Course Objectives: We will introduce statistical distributions and computing the statistical power of various designs, matrix algebra useful for statistics and the general linear model, maximum likelihood estimation and testing, Bayesian Statistics, and various resampling and randomization methods. The focus is obtaining a general understanding of these statistical tools rather than which computer programs to use. Thus, the course will be somewhat more theoretical than applied, but the student will leave with a much broader understanding than a course concerned with running various statistical packages.

Math/Stats background required: Some knowledge of Calculus and a previous stats course (which introduced covariance, regression and ANOVA) is desirable.

Meeting time and Place: Tuesday and Thursday, 8:00 am - 9:15 a.m. BSW 237

Instructor: Bruce Walsh:

Lecture schedule

This is the lecture schedule from the last time the course was taught. WHile all of the below topics will be covered, I also plan to add some news ones, such as logistic regression, and generalized linear models.

14-Jan Th 1 Overview: Probabilities, Variances, Covariances Univariate Distributions, Bivariate Distributions
18-Jan Tu 2 Normal, t, Chi-square, F distributions Distributions of functions of normals
20-Jan Th 3 Power of tests Power
25-Jan Tu 4 Hypothesis testing  
27-Jan Th 5 Matrix algebra 1: addition, multiplication Intro. to matrix algebra and linear models
1-Feb Tu 6 NO CLASS (in Washington DC)  
3-Feb Th 7 Matrix algebra 2: Inversion and the Multivariate Normal  
8-Feb Tu 8 Matrix algebra 3: Eigenstructure. Principal Components Eigenstructure Notes
10-Feb Th 9 General linear model (GLM)1: OLS General linear models
15-Feb Tu 10 GLM 2: GLS  
17-Feb Th 11 GLM 3: hypothesis testing  
22-Feb Tu 12 GLM 4: ANOVA ANOVA
24-Feb Th 13 GLM 5: Mixed Models Mixed Models, BLUP
29-Feb Tu 14 Maximum Likelihood (ML) 1: Introduction MLE and Likelihood ratio tests
2-Mar Th 15 NO CLASS (Meeting in DC)  
7-Mar Tu 16 ML 2: likelihood ratio tests and asymptotics  
9-Mar Th 17 ML 3: Mixture models Mixture Models
14-Mar Tu   SPRING BREAK!  
16-Mar Th   SPRING BREAK!  
21-Mar Tu 18 ML 4: Variance component models REML
23-Mar Th 19 Resampling methods 1: Randomization and the Jackknife Resampling methods
28-Mar Tu 20 Resampling methods 2: The Bootstrap
30-Mar Th 21 Bayesian methods: 1: Introduction Bayesian methods
4-Apr Tu   NO CLASS (Seminar in Florida)  
6-Apr Th   NO CLASS (Seminar in Florida)  
11-Apr Tu 22 Bayesian methods: 2 Posterior information  
12-Apr Th 23 Bayesian methods: 3: Estimation and hypothesis testing  
18-Apr Tu 24 Expectation-maximum (EM) methods 1: Introduction EM methods
20-Apr Th 25 EM methods 2: Treating missing data  
25-Apr Tu 26 Gibbs sampler 1: Bayesian applications MCMC and Gibbs
27-Apr Th 27 Gibbs sampler 2: ML  
2-May Tu 28 Generalized linear models  


Problem set Topic Due date Solutions
1 Simple Regressions 25 Jan PS 1 Solutions
2 Power of Normal tests 3-Feb PS 2 Solutions
3 Power and Non-central Fs 3-Feb PS 3 Solutions
4 Multivariate Normal 8-Feb PS 4 Solutions
5 General linear Model 24-Feb PS 5 Solutions
6 Random-Effects ANOVA 7 March PS 6 Solutions
7 Resampling Methods
Data: csv file
11 April

Selected Statistics Links

Patch for JMP IN 3.1.5 for Power Mac (Stuffit file, place both files in extensions folder)

On line Statistical tables (from UCLA) -- // -- Other statistical calculators

Comprehensive list of power analysis software for microcomputers

A collection of fun data sets for analysis can be found in the Journal of Statistics Education Data Archive

Home page for RNR613: , Applied Biostatistics.

Selected Statistics References

  1. Randomization, Boostrap and Monte Carlo methods in biology (2nd ed). Bryan F. J. Manly (1997).

  2. Bayesian Hierarchical Modeling David Draper. You can download a postscript file of the draft version from Draper's website

  3. Bayesian Statistics: An Introduction (2nd ed). Peter M. Lee (1997).

  4. Applying Generalized Linear Models. James K. Lindsey (1997).

  5. Tools for Statistical Inference: Methods for exploration of posterior distributions and likelihood functions (3rd ed). Martin Tanner (1996).

  6. Statistical Principles in Experimental Design (3rd ed). B. J. Winer, Donald R. Brown, and Kenneth M. Michels (1991).

  7. Intutive Biostatistics. Harvey Motulsky.

  8. Statistics as Principled Argument. Robert Abelson.

  9. Applying Generalized Linear Models. James K. Lindsey (1997).

  10. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference.Dani Gamerman (1997).

  11. The Ecological DetectiveRay Hilborn and Marc Mangel (1997).

  12. Mathematical and Statistical Methods for Genetic Analysis Keenth Lange (1997).

  13. Statistical Data Analysis. Glen Cowan (1998).

  14. Design and Analysis of Ecological Experiments. Samuel Scheiner and Jessice Gurevitch, Eds (1993).