Gary L. Gadbury Associate Professor   Dickens Hall, Room 107 Department of Statistics Kansas State University Manhattan, Kansas   66506 Office phone: (785)532-0526 FAX: (785)532-7736 E-mail: gadbury@ksu.edu

Statistics Summary

Statistics is a mathematical science and, at the same time, an applied science. Problems in the engineering and physical sciences, social and psychological sciences, biological and environmental sciences, and economic and political sciences all motivate new methods in statistics. As such, a statistician has ample opportunity to work within a number of scientific research areas. For more details regarding the statistics profession, see the homepage for the American Statistical Association.

Statistics at K-State

Research

Some research interests and projects are listed below, or see my Curriculum Vitae (pdf). My Ph.D in Statistics was awarded from Colorado State University in 1998.

Research into statistical methods for the analysis of gene expression data from microarrays.

Assessing individual treatment heterogeneity in randomized experiments.

Statistical methods for the analysis of data from metabolomics experiments.

Analysis of particulate emmissions from aircraft.

Information on Statistical Software

There are many software packages to perform statistical analysis of data. Two common products are S-Plus (R) and SAS. I use S-Plus (or R) for my research and consulting. Some links for S-plus and R are below.

Data and Story Library

The Homepage for "R"

Teaching

During the Spring Semester 2009, I will be teaching Stat 771, Theory of Statistics 2. Courses previously taught at University of Missouri - Rolla and Kansas State (and the primary text used) are listed below.

Applied Engineering Statistics (Navidi, Statistics for Engineers and Scientists)

Engineering Statistics (Devore, Probability and Statistics)

Biostatistics (Ramsey and Schafer, The Statistical Sleuth)

Probability and Statistics (Bain and Englehardt, Introduction fo Probability and Mathematical Statistics)

Mathematical Statistics (same as above but also Mood, Graybill, Boes, Introduction to the Theory of Statistics)

Regression Analysis (Kutner, Nachtsheim, Neter, Applied Linear Regression Models)

Statistical Data Analysis (Christensen, Analysis of Variance, Design, and Regression)

Nonparametric Statistical Methods (various texts and notes)

Linear Statistical Models I (Graybill, Theory and Application of the Linear Model)

Linear Statistical Models II (various texts and Graybill)

Introduction to SAS (various sources)

Topics in High Dimensional Data Analysis (various sources)

Regression and Correlation (Weisberg, Applied Linear Regression)

Applied Linear Statistical Models (Kutner, Nachtsheim, Neter, Li, Applied Linear Statistical Models)

Theory of Statistics 2 (Casella and Berger, Statistical Inference)

Other Links

K-State Department of Statistics

Institute of Mathematical Statistics

International Biometric Society, Eastern North American Region

The Section on Statistical Genetics, University of Alabama at Birmingham   (adjunct faculty appointment)

Yale Microarray Database