Elizabeth A. Claassen

Senior Associate Research Statistician Developer in the JMP Division of SAS

Elizabeth A. Claassen, PhD, is Senior Associate Research Statistician Developer in the JMP division of SAS. Dr. Claassen has 9 years’ experience with SAS software and 5 years’ experience with JMP. Her chief interest is generalized linear mixed models, and she brings to this work her expertise with SAS GLM, MIXED, GLIMMIX, and NLMIXED procedures for linear models. Dr. Claassen earned an MS and PhD in statistics from the University of Nebraska–Lincoln, where she received the Holling Family Award for Teaching Excellence from the College of Agricultural Sciences and Natural Resources.

Coming Soon!

Elizabeth Claassen | SAS Support

JMP® for Mixed Models

By Ruth M. Hummel, Elizabeth A. Claassen, and Russell D. Wolfinger
Anticipated publication date: Second quarter 2021

JMP® for Mixed Models is a comprehensive introduction to and reference manual on the use of mixed models with JMP software. The topics covered are analysis of simple, intermediate and complex experiments using mixed model methods in JMP, specifically DOE, single and multiple random effect models, repeated measures and spatial models, random coefficient models, and power analysis. Mixed models are essential for researchers and data analysts, and JMP is the ideal visual and intuitive tool, with a unique approach to graphical statistics. 

Back to Top

By This Author

SAS® for Mixed Models: An Introduction and Basic Applications

By Walter W. Stroup, George A. Milliken, Elizabeth A. Claassen, and Russell D. Wolfinger

Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS.

Back to Top