regression modeling methods theory and computation with sas

Download Book Regression Modeling Methods Theory And Computation With Sas in PDF format. You can Read Online Regression Modeling Methods Theory And Computation With Sas here in PDF, EPUB, Mobi or Docx formats.

Regression Modeling

Author : Michael Panik
ISBN : 9781420091984
Genre : Mathematics
File Size : 61. 48 MB
Format : PDF, ePub, Docs
Download : 268
Read : 902

Get This Book


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 squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs. A Comprehensive, Accessible Source on Regression Methodology and Modeling Requiring only basic knowledge of statistics and calculus, this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations.

Regression Modeling

Author : Michael Panik
ISBN : 1420091972
Genre : Mathematics
File Size : 89. 59 MB
Format : PDF, ePub, Mobi
Download : 602
Read : 623

Get This Book


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 squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs. A Comprehensive, Accessible Source on Regression Methodology and Modeling Requiring only basic knowledge of statistics and calculus, this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations.

Logistic Regression Using Sas

Author : Paul D. Allison
ISBN : 9781607649953
Genre : Mathematics
File Size : 41. 56 MB
Format : PDF, Docs
Download : 898
Read : 751

Get This Book


If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! 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. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models). This book is part of the SAS Press program.

Regression And Anova

Author : Keith E. Muller
ISBN : 1580258905
Genre : Computers
File Size : 50. 63 MB
Format : PDF
Download : 389
Read : 1161

Get This Book


This book provides a thorough and integrated treatment of multiple regression and ANOVA. The information it contains has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses on the general linear model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression and ANOVA than do traditional sums of squares and scalar equations. The book offers a balanced treatment of regression and ANOVA, yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text includes almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided into sections covering the following topics: basic theory; multiple regression; model building and evaluation; ANOVA; and ANCOVA.

Linear Regression Analysis

Author : Xin Yan
ISBN : 9789812834119
Genre : Mathematics
File Size : 63. 95 MB
Format : PDF
Download : 360
Read : 318

Get This Book


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 methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields.

Fixed Effects Regression Methods For Longitudinal Data Using Sas

Author : Paul D. Allison
ISBN : 1590477782
Genre : Mathematics
File Size : 20. 33 MB
Format : PDF
Download : 448
Read : 504

Get This Book


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. This book is part of the SAS Press program.

Design And Analysis Of Experiments

Author : Leonard C. Onyiah
ISBN : 9781420060553
Genre : Mathematics
File Size : 67. 82 MB
Format : PDF, ePub, Mobi
Download : 955
Read : 1068

Get This Book


Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory, it also explores regression approaches. Capitalizing on the availability of cutting-edge software, the author uses both manual methods and SAS programs to carry out analyses. The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation, the completely randomized design, the use of orthogonal contrast to test hypotheses, and the model adequacy check. With an emphasis on two-factor factorial experiments, the author analyzes repeated measures as well as fixed, random, and mixed effects models. He also describes designs with randomization restrictions, before delving into the special cases of the 2k and 3k factorial designs, including fractional replication and confounding. In addition, the book covers response surfaces, balanced incomplete block and hierarchical designs, ANOVA, ANCOVA, and MANOVA. Fortifying the theory and computations with practical exercises and supplemental material, this distinctive text provides a modern, comprehensive treatment of experimental design and analysis.

Multilevel Models

Author : Jichuan Wang
ISBN : 9783110267709
Genre : Mathematics
File Size : 50. 80 MB
Format : PDF, ePub, Docs
Download : 983
Read : 881

Get This Book


This book covers a broad range of topics about multilevel modeling. The goal is to help readers to understand the basic concepts, theoretical frameworks, and application methods of multilevel modeling. It is at a level also accessible to non-mathematicians, focusing on the methods and applications of various multilevel models and using the widely used statistical software SAS®. Examples are drawn from analysis of real-world research data.

Regression Modeling With Actuarial And Financial Applications

Author : Edward W. Frees
ISBN : 9780521760119
Genre : Business & Economics
File Size : 59. 42 MB
Format : PDF, ePub, Docs
Download : 463
Read : 1153

Get This Book


This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.

Sas For Data Analysis

Author : Mervyn G. Marasinghe
ISBN : 038777372X
Genre : Mathematics
File Size : 80. 66 MB
Format : PDF, ePub
Download : 143
Read : 811

Get This Book


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.

Top Download:

Best Books