 |
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
My experience with applications is fairly broad but with some focus on health, biology, and the environment. My
statistical software expertise is with S-Plus and R.
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
This Document is maintained by gadbury@ksu.edu Last Modified:
22-Jan-2009 14:00:42 CST URL: http://www-personal.ksu.edu/~gadbury/
![]() |