Understanding Regression and ANOVA as Research Tools Using SAS PDF Download

Author: Patricia F. Moodie
Publisher: Chapman and Hall/CRC
ISBN: 9781439869512
Category : Mathematics
Languages : en
Pages : 384

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Book Description
Designed for researchers and data analysts, this book presents rigorous statistical methods without burdening readers with mathematical theory. Readers see how linear regression and ANOVA can be used in biology, agriculture, health science, environmental science, fisheries, forestry, as well as the physical, social, and behavioral sciences. Each statistical topic is illustrated using detailed worked examples and relevant SAS procedures. The book emphasizes interpretation of analyses integrating graphical and statistical results and offers a detailed step-by-step guide for each analysis.

Author: Patricia F. Moodie
Publisher: CRC Press
ISBN: 0429527039
Category : Mathematics
Languages : en
Pages : 490

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Book Description
Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps. 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.

Author: Mervyn G. Marasinghe
Publisher: Springer
ISBN: 3319692399
Category : Computers
Languages : en
Pages : 688

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Book Description
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

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: Keith E. Muller
Publisher: SAS Press
ISBN: 9781580258906
Category : Analysis of variance
Languages : en
Pages : 0

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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: 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: Alan C. Elliott
Publisher: John Wiley & Sons
ISBN: 0470552646
Category : Education
Languages : en
Pages : 450

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Book Description
SAS Essentials provides an introduction to SAS statistical software, the premiere statistical data analysis tool for scientific research. Through its straightforward approach, the text presents SAS with step-by-step examples. With over fifteen years of teaching SAS courses and over fifty combined years of teaching and consulting by the authors, this valuable reference presents data manipulation and statistical techniques, including a website with examples. This textbook is essential for teachers because the chapters are self-contained and may be used accordingly to the teacher?s preference, whether for a one-semester or two-semesters course.

Author: Ken Kleinman
Publisher: CRC Press
ISBN: 1466584491
Category : Mathematics
Languages : en
Pages : 473

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Book Description
An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

Author: Ramon Littell
Publisher: John Wiley & Sons
ISBN: 0471221740
Category : Mathematics
Languages : en
Pages : 500

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Book Description
Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related features, and new material, including a chapter on generalised linear models. Version 8 of the SAS System was used to run the SAS code examples in the book. * Provides clear explanations of how to use SAS to analyse linear models * Includes numerous SAS outputs * Includes new chapter on generalised linear models * Uses version 8 of the SAS system This book assists data analysts who use SAS/STAT software to analyse data using regression analysis and analysis of variance. It assumes familiarity with basic SAS concepts such as creating SAS data sets with the DATA step and manipulating SAS data sets with the procedures in base SAS software.

Author: Rudolf Jakob Freund
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 248

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Book Description
Some basic statistics: a review; Elements of a SAS program; Regression; Statistical background; Implementing GLM for regression; Other topics; Creating data; Multicollinearity; Analysis of means; One- and two-sample tests and statistics; Comparison of several means: the analysis of variance; Analysis-of-variance models of less than full rank; The dummy-variable model; Two-way structure; Higher-order structures; Nested structure; Proper error terms; Estimable functions; Examples of special applications; Covariance and the heterogeneity of slopes; A one-way structure; Two-way structure without interaction; Two-way structure with interaction; Heterogeneity of slopes; Multivariate linear models; A one-way structure; A two-factor factorial; Multivariate analysis of covariance.