Regression methods

a tool for data analysis
  • 261 Pages
  • 2.25 MB
  • 4558 Downloads
  • English
by
M. Dekker , New York
Regression anal
StatementRudolf J. Freund, Paul D. Minton.
SeriesStatistics, textbooks and monographs ; v. 30
ContributionsMinton, Paul D., 1918- joint author.
Classifications
LC ClassificationsQA278.2 .F7
The Physical Object
Paginationxi, 261 p. :
ID Numbers
Open LibraryOL4416490M
ISBN 100824766474
LC Control Number79020458

This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal Regression methods book hierarchical outcomes, and generalized linear models for counts and other /5(13).

An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels.

It is also a valuable reference for practicing statisticians, engineers, and physical by: Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods.

The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied. The organization of th. The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression.

Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression : Thomas P. Ryan. ing the methods discussed in the chapter (as well as appropriate material from earlier chapters), including both methodological and graphical Regression methods book.

The book begins with discussion of the multiple regression model. Many regression textbooks start with discussion of. Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods.

The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex. Book Description.

Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied.

In this book, we mainly concentrate on the major non-parametric regression and smoothing methods including local polynomial, regression spline, smoothing spline and penalized spline Chapter 1 provides a brief overview of the book chapters, and in particular, presents data examples from biomedical research studies which have motivated the use of.

Bayesian regression. I find Bayesian stuff conceptually hard, so I am using John Kruschke’s friendly book: “Doing Bayesian data analysis”. Chapter 16 is on linear regression. He provides a free R package to carry out all the analyses Regression methods book the book.

The CRAN view “Bayesian” has many other suggestions. Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in This new edition has been updated and enhanced to include all-new information on the.

A regression analysis starts with an estimate of the population mean(s) using a mathematical formula, called a “function,” which explains the relationship between the predictor variable(s) and the response variable.

Statistical Methods, The book includes research projects, real-world case studies, numerous examples, and data. 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.

Download Regression methods PDF

Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on.

This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other Book Edition: 2nd Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis.

Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in. Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory:.

For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice."--Paul E.

Spector, University of South Florida "As a quantitative methods instructor, I have reviewed and used many statistical textbooks. STAT is an applied linear regression course that emphasizes data analysis and interpretation. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features').

The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more. Modern Methods for Robust Regression offers a brief but in-depth treatment of various methods for detecting and properly handling influential cases in regression analysis.

This volume, geared toward both future and practicing social scientists, is unique in that it takes an applied approach and offers readers empirical examples to illustrate key concepts.

Details Regression methods FB2

Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are by: 5.

Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do.

Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly about linear least squares applied to fit a single equation (as opposed to a 5/5(2).

This book really does cover everything you ever wanted to know about logistic regression with updates available on the author’s website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods.

“This book is dedicated to describing the Bayesian and frequentist regression methods and to illustrating the use of these methods. This book could be used for three separate graduate courses: regression methods for independent data; regression methods for dependent data; and nonparametric regression and classification.

the book would Brand: Springer-Verlag New York. The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application.

The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle.

This is not a book about the theory of regression.

Description Regression methods FB2

It is about using regression to solve real problems of. In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM).

Readers will be. Cherie Berry’s Elevator Pictures, Spatial Regression, and the Ups and Downs of the Research Process Empathizing With Suffering vs. Empathizing With Joy: Establishing the Separability of Distinct yet Overlapping Constructs Using Both Experimental and Correlational Methods.

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust. Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of.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.The book is based on the author's experience teaching a graduate sequence in regression methods.

An introductory chapter describes a number of motivating examples and discusses general issues that need consideration before a regression analysis is carried out.