Rudolf J. Freund, Ph.D., former associate director and cofounder of the Department of Statistics at Texas A&M University, is now professor emeritus. Dr. Freund received an M.A. degree in economics from the University of Chicago in 1951 and a Ph.D. in statistics at North Carolina State College in 1955. He has coauthored several impressive books including Regression Methods, SAS® System for Linear Models, Third Edition, Statistical Methods, and Regression Analysis. Dr. Freund has been a SAS user since 1972 and is a former SUGI chairman.
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 the two previous editions of this book with Dr. Freund and is also widely published in statistical and applied journals. Dr. Littell is also coauthor of SAS® System for Linear Models, Third Edition, SAS® System for Mixed Models, and SAS® System for Elementary Statistical Analysis, Second Edition. A SAS user since 1972, Dr. Littell has served as SUGI chairman and is a Fellow of the American Statistical Association.
Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include
Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed.
New for this edition
The Third Edition includes revisions, updated material, and new material. You’ll find new information on
Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
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