JMP for Mixed Models

by Ruth Hummel, Elizabeth A. Claassen, Russell D. Wolfinger

Overview

Discover the power of mixed models with JMP and JMP Pro.

Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions.

JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP’s groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code.

Inside you’ll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include:

• Learning how to appropriately recognize, set up, and interpret fixed and random effects
• Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs
• Understanding how degrees of freedom work using Skeleton ANOVA
• Analyzing randomized block, split-plot, longitudinal, and repeated measures designs
• Introducing more advanced methods such as spatial covariance and generalized linear mixed models
• Simulating mixed models to assess power and other important sampling characteristics
• Providing a solid framework for understanding statistical modeling in general
• Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference

Product Details

ISBN-13:	9781952363856
Publisher:	SAS Institute
Publication date:	06/09/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	262
File size:	27 MB
Note:	This product may take a few minutes to download.

About the Author

Ruth M. Hummel, PhD, is a Senior Manager of Analytical Education at SAS. Dr. Hummel develops curricula, teaches, and consults to help researchers and practitioners apply statistical methods and analytics to solving problems, predominantly in the health and life sciences. Prior to joining SAS in 2016, she worked at the Environmental Protection Agency as the statistical expert for the Risk Assessment Division of the Office of Pollution Prevention and Toxics, and she taught and consulted at the Pennsylvania State University and at the University of Florida. Dr. Hummel is a co-author of Business Statistics and Analytics in Practice, 9th edition, a business statistics textbook emphasizing simple data mining techniques earlier in the standard curriculum. She has a PhD in statistics from the Pennsylvania State University.
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.
Russell D. Wolfinger, PhD, is Director of Scientific Discovery and Genomics at SAS, a department he started, where he leads research and development of JMP and SAS software solutions in the domains of genomics and clinical trials. Dr. Wolfinger devoted 10 years to developing and promoting SAS statistical procedures for mixed models and multiple testing. He has co-authored more than 100 publications, including three books. He is a Kaggle Grandmaster, as well as a Fellow of both the American Association for the Advancement of Science and the American Statistical Association.