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Author: Rudolf Freund
Publisher: John Wiley & Sons
ISBN: 0471416649
Category : Mathematics
Languages : en
Pages : 258
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Book Description
SAS® System for Regression 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 performing linear regression analyses using PROC REG 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 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 using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. 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.
Author: Rudolf Freund
Publisher: John Wiley & Sons
ISBN: 0471416649
Category : Mathematics
Languages : en
Pages : 258
Get Book Here
Book Description
SAS® System for Regression 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 performing linear regression analyses using PROC REG 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 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 using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. 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.
Author: Vivek Ajmani
Publisher: John Wiley & Sons
ISBN: 1118210328
Category : Mathematics
Languages : en
Pages : 414
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Book Description
The first cutting-edge guide to using the SAS® system for the analysis of econometric data Applied Econometrics Using the SAS® System is the first book of its kind to treat the analysis of basic econometric data using SAS®, one of the most commonly used software tools among today's statisticians in business and industry. This book thoroughly examines econometric methods and discusses how data collected in economic studies can easily be analyzed using the SAS® system. In addition to addressing the computational aspects of econometric data analysis, the author provides a statistical foundation by introducing the underlying theory behind each method before delving into the related SAS® routines. The book begins with a basic introduction to econometrics and the relationship between classical regression analysis models and econometric models. Subsequent chapters balance essential concepts with SAS® tools and cover key topics such as: Regression analysis using Proc IML and Proc Reg Hypothesis testing Instrumental variables analysis, with a discussion of measurement errors, the assumptions incorporated into the analysis, and specification tests Heteroscedasticity, including GLS and FGLS estimation, group-wise heteroscedasticity, and GARCH models Panel data analysis Discrete choice models, along with coverage of binary choice models and Poisson regression Duration analysis models Assuming only a working knowledge of SAS®, this book is a one-stop reference for using the software to analyze econometric data. Additional features include complete SAS® code, Proc IML routines plus a tutorial on Proc IML, and an appendix with additional programs and data sets. Applied Econometrics Using the SAS® System serves as a relevant and valuable reference for practitioners in the fields of business, economics, and finance. In addition, most students of econometrics are taught using GAUSS and STATA, yet SAS® is the standard in the working world; therefore, this book is an ideal supplement for upper-undergraduate and graduate courses in statistics, economics, and other social sciences since it prepares readers for real-world careers.
Author: Samprit Chatterjee
Publisher: John Wiley & Sons
ISBN: 1119122732
Category : Mathematics
Languages : en
Pages : 421
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Book Description
Praise for the Fourth Edition: "This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable." —Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
Author: Keith E. Muller
Publisher: Wiley-SAS
ISBN:
Category : Computers
Languages : en
Pages : 582
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Book Description
Muller and Fetterman (U. of N. Carolina, Chapel Hill) developed this text for use in "Intermediate Linear Models," a graduate level biostatistics class at UNC, covering basic theory, multiple regression, model building and evaluation, ANOVA, and universal tools. The text uses sets of real data, and contains almost no proofs. Ideal prerequisites for use include a matrix algebra class, an undergraduate introduction to mathematical statistics, basic programming skills in the statistical package used in the course (data input, data transformation, and analysis), and basic skills in linear models. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com).
Author:
Publisher:
ISBN: 9781642954876
Category :
Languages : en
Pages : 142
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Book Description
This special collection of SAS Global Forum papers demonstrates new and enhanced capabilities and applications of lesser-known SAS/STAT and SAS Viya procedures for regression models. The goal here is to raise awareness of current valuable SAS/STAT content of which the user may not be aware. Also available free as a PDF from sas.com/books.
Author: Xin Yan
Publisher: World Scientific
ISBN: 9812834109
Category : Mathematics
Languages : en
Pages : 349
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Book Description
"This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area." --Book Jacket.
Author: Walter W. Stroup
Publisher: SAS Institute
ISBN: 163526152X
Category : Computers
Languages : en
Pages : 823
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Book Description
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.
Author: A. John Bailer
Publisher: CRC Press
ISBN: 1000734927
Category : Business & Economics
Languages : en
Pages : 379
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Book Description
Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.
Author: Mervyn G. Marasinghe
Publisher: Springer Science & Business Media
ISBN: 038777372X
Category : Mathematics
Languages : en
Pages : 562
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Book Description
This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are p- marily graduate majors or advanced undergraduate students from a variety of disciplines. These students typically have taken an introductory-level s- tistical methods course that requires the use a software system such as SAS for performing statistical analysis. Thus students are expected to have an - derstanding of basic concepts of statistical inference such as estimation and hypothesis testing. Understandably, adequate time is not available in a ?rst course in stat- tical methods to cover the use of a software system adequately in the amount of time available for instruction. The aim of this book is to teach how to use the SAS system for data analysis. The SAS language is introduced at a level of sophistication not found in most introductory SAS books. Important features such as SAS data step programming, pointers, and line-hold spe- ?ers are described in detail. The powerful graphics support available in SAS is emphasized throughout, and many worked SAS program examples contain graphic components.
Author: Charles DiMaggio
Publisher: Springer Science & Business Media
ISBN: 1461448549
Category : Mathematics
Languages : en
Pages : 266
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Book Description
This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a “hands on” approach to the use of SAS for a broad number of epidemiologic analyses, readers learn techniques for data entry and cleaning, categorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps for successful programming. SAS (Statistical Analysis System) is an integrated system of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engage in many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widely used in the fields of epidemiology and public health research, predominately due to its ability to reliably analyze very large administrative data sets, as well as more commonly encountered clinical trial and observational research data.
