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Exploring Modern Regression Methods Using SAS
Author:
Publisher:
Published: 2019-06-21
Total Pages: 142
ISBN-13: 9781642954876
DOWNLOAD EBOOK →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.
A Modern Approach to Regression with R
Author: Simon Sheather
Publisher: Springer Science & Business Media
Published: 2009-02-27
Total Pages: 398
ISBN-13: 0387096086
DOWNLOAD EBOOK →This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.
Regression Modeling
Author: Michael Panik
Publisher: CRC Press
Published: 2009-04-30
Total Pages: 832
ISBN-13: 1420091980
DOWNLOAD EBOOK →Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least square
Building Regression Models with SAS
Author: Robert N. Rodriguez
Publisher: SAS Institute
Published: 2023-04-18
Total Pages: 464
ISBN-13: 1951684001
DOWNLOAD EBOOK →Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.
Fixed Effects Regression Methods for Longitudinal Data Using SAS
Author: Paul D. Allison
Publisher:
Published: 2019-07-12
Total Pages: 160
ISBN-13: 9781642953237
DOWNLOAD EBOOK →Fixed Effects Regression Methods for Longitudinal Data Using SAS, written by Paul Allison, is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, and PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.
Discovering Statistics Using SAS
Author: Andy Field
Publisher: SAGE
Published: 2010-01-30
Total Pages: 753
ISBN-13: 1446242234
DOWNLOAD EBOOK →Hot on the heels of the 3rd edition of Andy Field′s award-winning Discovering Statistics Using SPSS comes this brand new version for students using SAS®. Andy has teamed up with a co-author, Jeremy Miles, to adapt the book with all the most up-to-date commands and programming language from SAS® 9.2. If you′re using SAS®, this is the only book on statistics that you will need! The book provides a comprehensive collection of statistical methods, tests and procedures, covering everything you′re likely to need to know for your course, all presented in Andy′s accessible and humourous writing style. Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS®. A ′cast of characters′ supports the learning process throughout the book, from providing tips on how to enter data in SAS® properly to testing knowledge covered in chapters interactively, and ′real world′ and invented examples illustrate the concepts and make the techniques come alive. The book′s companion website (see link above) provides students with a wide range of invented and real published research datasets. Lecturers can find multiple choice questions and PowerPoint slides for each chapter to support their teaching.
Logistic Regression Using SAS
Author: Paul D. Allison
Publisher: SAS Institute
Published: 2012-03-30
Total Pages: 348
ISBN-13: 1629590185
DOWNLOAD EBOOK →Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Includes several real-world examples in full detail.
Data Mining and Exploration
Author: Chong Ho Alex Yu
Publisher: CRC Press
Published: 2022-10-27
Total Pages: 290
ISBN-13: 1000777790
DOWNLOAD EBOOK →This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Applied Medical Statistics Using SAS
Author: Geoff Der
Publisher: CRC Press
Published: 2012-10-01
Total Pages: 562
ISBN-13: 1439867976
DOWNLOAD EBOOK →Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data. Features Covers the planning stage of medical studies in detail; several chapters contain details of sample size estimation Illustrates methods of randomisation that might be employed for clinical trials Covers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputation Its breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health. Complete data sets, all the SAS code, and complete outputs can be found on an associated website: http://support.sas.com/amsus
