Home page for EEB 596Z:
Issues in Biostatistical Analysis

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

Current student information

Problem Set Five is due 28 march 2002.

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.

Computer Programs: While the course focus is in basic statistical concepts, we will also introduce two programs:

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

Instructor: Bruce Walsh:

The R Statistical Programming Language

The R Project for statistical Computing website

US Mirror site for downloading R. Current versions for

An Introduction of R (Walsh notes)

  1. R as a basic statistical calculator for obtaining p values and plotting probability distributions (6 page pdf file). [ posted 28 Dec. 2001 ]
  2. Power Calculations in R (4 page pdf file). [ posted 18 Jan 2002 ]
  3. Matrix Calculations in R (3 page pdf file). [ posted 14 Feb 2002 ]

pdf files of The official R Manuals

Lecture schedule

(tentative, topics may be added/deleted per wishes of class)

DATE Day Lect. # Topic misc handouts
10 Jan Thursday 1 Overview: Probabilities, Variances, Covariances   (1): Univariate Distributions,

(2): Bivariate Distributions

15 Jan Tuesday 2 Normal, t, Chi-square, F distributions   (1): Distributions of functions of normals,

(2): R as a basic statistical calculator

17 Jan Thursday 3 Power of tests 1: Normals Problem Set One due (1): Power,

(2): Simple power calculations in R

22 Jan Tuseday 4 Power of tests 2: ANOVAs    
24 Jan Thursday 5 Matrix algebra 1: addition, multiplication Problem Set Two due Intro to Matrix Algebra and linear models
29 Jan Tuseday 6 Matrix algebra 2: Inversion and the Multivariate Normal Problem Set Three due Matrix Calculations in R
31 Jan Thursday 7 Matrix algebra 3: Eigenstructure. Principal Components   Eigenstructure Notes
5 Feb Tuseday 8 General linear model (GLM)1: OLS   General linear models
7 Feb Thursday 9 GLM 2: GLS Problem Set Four due  
12 Feb Tuesday 10 GLM 3: hypothesis testing    
14 Feb Thursday 11   Walsh at NIH  
19 Feb Tuesday 12Generalized Linear Models   Generalized Linear Models
21 Feb Thursday 13 ANOVA   ANOVA
21 Feb Tuesday 14 Mixed Models   Mixed Linear Models
26 Feb Thursday 15 Maximum Likelihood (ML) 1: Introduction   MLE and Likelihood ratio tests
28 Feb Tuesday 16 ML 2: likelihood ratio tests and asymptotics    
5 March Thursday 17 ML 3: Mixture models    
12 march Tuesday   Spring Break    
14 March Thursday   Spring Break    
19 March Tuesday 18 No Class    
21 March Thursday 19 No Class    
26 March Tuesday 20 No Class    
28 March Thursday 21 Resampling methods 1: Randomization and the Jackknife Problem Set Five due Resampling methods
2 April Tuesday 22 Resampling methods 2: The Bootstrap    
4 April Thursday 23 Bayesian methods: 1: Introduction   Bayesian methods
9 April Tuesday 24 Bayesian methods: 2 Posterior information    
11 April Thursday 25 Bayesian methods: 3: Estimation and hypothesis testing    
16 April Tuesday 26 Gibbs sampler: Bayesian applications   MCMC and Gibbs
18 April Thursday 27 Bayesian methods: 3: Estimation and hypothesis testing    
23 April Tuesday 28 Gibbs sampler: Bayesian applications    
25 April Thursday 29     MCMC and Gibbs
30 April Tuesday 30 Expectation-maximum (EM) methods 1: Treating missing data    


Problem set Topic Due date Solutions
1 Simple Regressions 17 Jan 2002 PS 1 Solutions
2 Power of Normal tests 24 Jan 2002 PS 2 Solutions
3 Power and Non-central Fs 29 Jan 2002 PS 3 Solutions
4 Multivariate normal calculations 7 Feb 2002 PS 4 Solutions
5 Univariate Maximum Likelihood 28 March 2002 PS 5 Solutions

Selected Statistics Links

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

The StatLib site at the Department of Statistics, Carnegie Mellon University.

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

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

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

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

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

  14. Regression Modeling Strategies. Frank E. Harrell, Jr. (2001).