Regression Using JMP
Thorough discussion of the following is also presented:
* confidence limits
* polynomial and smoothing models
* examples using JMP?? scripting language
* regression in the context of linear model methodology
* diagnosis of and remedies for data problems including outliers and collinearity
Statistical consultants familiar with regression analysis and with basic JMP concepts will appreciate the conversational, "what to look for" and "what if" scenarios presented. Non-Statisticians with a working knowledge of statistical concepts will learn to use JMP successfully for each analysis.
Using This Book.
1. Regression Concepts.
2. Regressions in JMP.
4. Collinearity: Detection and Remedial Measures.
5. Polynomial and Smoothing Models.
6. Special Applications of Linear Models.
7. Nonlinear Models.
8. Regression with JMP Scripting Language.
Ramon C. Littell, Ph.D., is professor of statistics at the University of Florida, where he teaches applied statistics and serves as consulting statistician in the Institute of Food and Agricultural Sciences. Dr. Littell is coauthor of SAS® System for Regression, Third Edition; SAS® System for Linear Models, Third Edition; SAS® System for Mixed Models; and SAS® System for Elementary Statistical Analysis, Second Edition. He is also widely published in statistical and applied journals. A SAS user since 1972, Dr. Littell has served as SUGI chairman and is a Fellow of the American Statistical Association.
Lee Creighton, Ph.D., is the documentation manager for the JMP division at SAS institute. He received his undergraduate degrees in mathematics education from North Carolina State University. His interest include the methods of teaching statistics and the applications of statistics to education measurement. Prior to working at SAS, Dr. Creighton taught high school and collegiate mathematics in North Carolina. He is coauthor of JMP® Start Statistics.