Sas System For Regression

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Sas System For Regression

Sas System For Regression
Author: Rudolf Jakob Freund
Publisher: SAS Press
ISBN: 9781580257251
Size: 15.18 MB
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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 include performing linear regression analyses using PROC REG and diagnosing and providing remedies for data problems, including outliers and multicollinearity. 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 SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.
SAS System for Regression
Language: en
Pages: 236
Authors: Rudolf Jakob Freund, Ramon C. Littell
Categories: Computers
Type: BOOK - Published: 2000 - Publisher: SAS Press
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 include performing linear regression analyses using PROC REG and diagnosing and providing remedies for data problems, including outliers and multicollinearity. 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 SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.
SAS System for Regression
Language: en
Pages: 264
Authors: Rudolf J. Freud, Ph.D., Ramon C. Littell, Ph.D.
Categories: Computers
Type: BOOK - Published: 2000-10 - Publisher: SAS Institute
Learn to perform a wide variety of regression analyses using SAS software with this example-driven favorite from SAS Publishing. With SAS System for Regression, Third Edition, you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression analyses using PROC REG and diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. Authors Rudolf Freund and Ramon Littell supply a helpful discussion of theory where necessary. Some knowledge of both regression and SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.
SAS System for Regression + Applied Regression Modeling
Language: en
Pages:
Authors: Rudolf Freund, Ramon Littell, Iain Pardoe
Categories: Mathematics
Type: BOOK - Published: 2008-03-14 - Publisher: Wiley-Interscience
This set contains: 9780471416647 SAS System for Regression by Rudolf Freund, Ramon Little and 9780471970330 Applied Regression Modeling: A Business Approach, Third Edition by Iain Pardoe
SAS System for Mixed Models
Language: en
Pages: 633
Authors: Ramon C. Littell, George A. Milliken, Walter W. Stroup, Russell D. Wolfinger
Categories: Computers
Type: BOOK - Published: 1996 - Publisher: SAS Institute
At last! A comprehensive, applications-oriented mixed models guide for data analysis. Discover the latest capabilities available for a wide range of applications featuring the MIXED procedure in SAS/STAT software.
Logistic Regression Using the SAS System
Language: en
Pages: 288
Authors: Paul David Allison
Categories: Computers
Type: BOOK - Published: 1999 - Publisher: SAS Institute
If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, this book is for you Informal and nontechnical, Paul Allison's Logistic Regression Using SAS: Theory and Application both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using SAS. Several social science real-world examples are included in full detail. The book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis with the PHREG procedure, and Poisson regression. Other highlights include discussions of how to use the GENMOD procedure to do log-linear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed.
Logistic Regression Examples Using the SAS System
Language: en
Pages: 163
Authors: SAS Institute
Categories: Computers
Type: BOOK - Published: 1995 - Publisher: Sas Inst
Data set examples show how to create a SAS data set for the raw data and print the data set.
SAS System for Elementary Statistical Analysis
Language: en
Pages: 440
Authors: Sandra D. Schlotzhauer, Ramon C. Littell
Categories: Computers
Type: BOOK - Published: 1997 - Publisher: Sas Inst
This updated edition shows how to use SAS to perform basic statistical analysis. General topics include creating a data set with SAS; summarizing data with descriptive statistics, frequency tables, and bar charts; comparing groups (t-tests, one-way ANOVA, and nonparametric analogues); performing basic linear regression (lines, curves, and two-variable models); performing simple regression diagnostics (residuals plots, studentized residuals); and creating and analyzing tables of data. Using real-life examples, this beginner's guide bridges the gap between statistics texts and SAS documentation.
SAS for Linear Models, Fourth Edition
Language: en
Pages: 492
Authors: Ramon C. Littell, Ph.D., Walter W. Stroup, Ph.D., Rudolf J. Freund, Ph.D.
Categories: Mathematics
Type: BOOK - Published: 2002-03-22 - Publisher: SAS Institute
This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material. This book is part of the SAS Press program.
SAS System for Forecasting Time Series
Language: en
Pages: 240
Authors: John C. Brocklebank, David A. Dickey, SAS Institute
Categories: Computers
Type: BOOK - Published: 1986 - Publisher: Sas Inst
This book shows how SAS performs multivariate time series analysis and features the advanced SAS procedures STATESPACE, ARIMA, and SPECTRA, The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analyzed and results desired. Using this book you will learn to model and forecast simple auto regressive (AR) processes using (PROC ARIMA and use the STATESPACE procedure and the AR model to do state space modeling. Other topics covered include detecting sinusoidal components in time series models and performing bivariate cross-spectral analysis and comparing the results the standard transfer function methodolgoy.
SAS for Mixed Models, Second Edition
Language: en
Pages: 828
Authors: Ramon C. Littell, Ph.D., George A. Milliken, Ph.D., Walter W. Stroup, Ph.D., Russell D. Wolfinger, Ph.D., Oliver Schabenberger, Ph.D.
Categories: Mathematics
Type: BOOK - Published: 2007-06-25 - Publisher: SAS Institute
The indispensable, up-to-date guide to mixed models using SAS. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures in SAS for Mixed Models, Second Edition, the comprehensive mixed models guide for data analysis, completely revised and updated for SAS 9 by authors Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger. The theory underlying the models, the forms of the models for various applications, and a wealth of examples from different fields of study are integrated in the discussions of these models: random effect only and random coefficients models; split-plot, multilocation, and repeated measures models; hierarchical models with nested random effects; analysis of covariance models; spatial correlation models; generalized linear mixed models; and nonlinear mixed models. Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered. This book is part of the SAS Press program.