
Applied Regression and ANOVA Using SAS
Description
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Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided.
Features:
*Statistical concepts presented in words without matrix algebra and calculus
*Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics
*Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection
*Suggestions of alternative approaches when a method's ideal inference conditions are unreasonable for one's data
This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.
Reviews / Votes
"... A must for someone that wants to work with theaforementioned models using SAS and wants a step-by-step guide on how and when toimplement those models. Each chapter is organized in a very similar manner. Itprovides theminimum amount of theory in a non-technical way at first, including when to use a specificmodel, what should be checked as assumptions and what to do when assumptions are not met."David Manteigas, ISCB News, May 2024
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Persons
Dallas E. Johnson, Professor Emeritus in the Department of Statistics, Kansas State University, has published extensively in the areas of linear models, multiplicative interaction models, experimental design, and messy data analysis. He is the author of Applied Multivariate Methods for Data Analysts and co-author with George A. Milliken of the following books: Analysis of Messy Data, Vol. I - Designed Experiments, Vol. II - Nonreplicated Experiments, Vol. III - Analysis of Covariance, and Vol. I - Designed Experiments 2nd Edition. An active presenter of short courses, and a statistical consultant for over 50 years, he was the recipient of ASA's award for Excellence in Statistical Consulting in 2010. He received his B.S. degree in Mathematics Education, Kearney State College, a M.A.T. degree in Mathematics, Colorado State University, a M.S. degree in Mathematics, Western Michigan University, and a Ph.D. degree in Statistics, Colorado State University. He has been a SAS user and mentor since 1976.
Content
2. Introduction to Simple Linear Regression
3. Model Checking in Simple Linear Regression
4. Interpreting a Simple Linear Regression Analysis
5. Introduction to Multiple Linear Regression
6. Before Interpreting A Multiple Linear Regression
7. Additive Multiple Linear Regression
8. Two-Way Interaction Between Continuous Predictors
9. Qualitative and Continuous Predictor Interaction
10. Predictor Subset Selection
11. Evaluating Equality of Group Means
12. Simultaneous Inference
13. Adjusting Group Means for Nuisance Variables
14. Alternative Approaches
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