Jim Stapleton

429A Wells
(517) 355-9678
Fax: 517-432-1405
Mailing Address: Department of Statistics and Probability
Michigan State University
East Lansing, MI 48824

Professor of Statistics and Probability
Graduate Director
B.A. (Mathematics and Education) Eastern Michigan University (1952)
M.S. (Mathematical Statistics) Purdue University (1954)
PhD (Mathematical Statistics) Purdue University (1957)

Text by the author, "Linear Statistical Models",
John Wiley & Sons, 1995, 450 pages

(From the Back of the Book) "Linear Statistical Models" emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites are familiarity with linear algebra, and probability at the postcalculus level.
Major topics covered include:

Methods of study of random vectors, incuding the multivariate normal, chi-square, t and F distributions, central and noncentral.
The linear model and the basic theory of regression analysis and the analysis of variance
Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap.
Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer.
Analysis of variance, with two- and three-way analysis of variance.
Random component models, nested designs, and balanced incomplete block designs.
Analysis of frequency data through log-linear models, with emphasis on the vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.

An errata list may be obtained by writing, calling or E-Mailing the author.

1999-2000 Academic Year Courses

Statistics 841, Fall Semester
"Linear Statistical Models"
STT 841

Statistics 231, Spring Semester
"Statistics for Scientists"

Links To: