Math 496

Statistical Computing

Here is your Take-home final exam.

Look for more lecture notes Friday morning.

The primary goal of the course will be to teach you to be effective users of the statistical package S-PLUS. We will cover most topics in the textbook, An Introduction to S and S-PLUS by Phil Spector. A second set of goals will be to gain exposure and understanding of many important and modern topics in statistical computing, including algorithms for regression, pseudo-random number generation, the bootstrap, cross-validation, and Markov chain Monte Carlo. Since this is a wide assortment of advanced topics, we will only skim the basics for most of them.

Course notes and other information useful to you in Professor Larget's Statistical Computing course are available on-line. Click on highlighted words to access the relevant information.

Course Information

Meeting Times and Places

The class meets on Mondays from 8:00 to 8:50 A.M. and from 1:15 to 2:00 P.M. Also we meet from 1:15 to 3:00 P.M. on Wednesdays. The regular classroom is Bayer 203, although we will frequently meet in the Alcoa Computer Lab.

Remaining Schedule

April 1 Tests of Pseudo-random Number Generators (am lab, pm classroom)
April 2 Generating pseudo-random numbers from other distributions (classroom)
April 7 Regression in S-PLUS and in the computer (am lab, pm classroom)
April 9 The bootstrap and cross-validation (classroom)
April 14 ANOVA in S-PLUS, catch-up (am lab, pm classroom)
April 16 Regression and ANOVA in JMP (lab)
April 21 Markov Chains and MCMC (classroom)
April 23 Markov Chains and MCMC (classroom)
April 28 Review for Final (classroom)

Lecture Notes

All lecture notes Copyright (c) 1997 Bret Larget. All rights reserved.

I will post course lecture notes here shortly prior to covering the material in class. The material on how to use S-PLUS is well documented in the textbook and in the on-line help. I will use a variety of other sources to compile notes for the other topics in this course and put these notes here.

Your S-PLUS environment An Introduction to S-PLUS A Bare Bones Introduction to UNIX Data in S-PLUS
Algorithms for the Mean and Variance Generating Uniform Pseudo-random Numbers Generating Pseudo-random Numbers from Other Distributions Regression and the QR decomposition
Regression in S-PLUS An ANOVA Example in S-PLUS The Bootstrap Markov Chain Monte Carlo


Homework #1 Homework #2 Homework #3 Homework #4 Homework #5

Last modified: May 2, 1997

Bret Larget,