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Regression with Linear Predictors, Per Kragh Andersen; Lene Theil Skovgaard


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Автор: Per Kragh Andersen; Lene Theil Skovgaard
Название:  Regression with Linear Predictors
ISBN: 9781441971692
Издательство: Springer
Классификация:
ISBN-10: 1441971696
Обложка/Формат: Hardcover
Страницы: 494
Вес: 0.88 кг.
Дата издания: 23.07.2010
Серия: Statistics for Biology and Health
Язык: English
Размер: 234 x 156 x 29
Читательская аудитория: General (us: trade)
Основная тема: Statistics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression, - gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear, logistic, proportional hazards, andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor: thatis, t-tests, one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2 2, 2 (k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model held ?xed.

Regression Analysis and Linear Models: Concepts, Applications, and Implementation

Автор: Darlington Richard B., Hayes Andrew F.
Название: Regression Analysis and Linear Models: Concepts, Applications, and Implementation
ISBN: 1462521134 ISBN-13(EAN): 9781462521135
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Описание: Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book`s examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable`s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Applied Regression Analysis and Generalized Linear Models

Автор: Fox John
Название: Applied Regression Analysis and Generalized Linear Models
ISBN: 1452205663 ISBN-13(EAN): 9781452205663
Издательство: Sage Publications
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Цена: 166850.00 T
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Описание: Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.

Applied Linear Regression

Автор: Weisberg Sanford
Название: Applied Linear Regression
ISBN: 1118386086 ISBN-13(EAN): 9781118386088
Издательство: Wiley
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Цена: 125610.00 T
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Описание: Providing a coherent set of basic methodology for applied linear regression without being encyclopedic, the fourth edition of Applied Linear Regression is thoroughly updated to help students master the theory and applications of linear regression modeling.

Regression, ANOVA, and the General Linear Model

Название: Regression, ANOVA, and the General Linear Model
ISBN: 1412997356 ISBN-13(EAN): 9781412997355
Издательство: Sage Publications
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Цена: 140450.00 T
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Описание: The author demonstrates basic statistical concepts from two different perspectives, giving the reader a conceptual understanding of how to interpret statistics and their use

Applied Linear Regression, 3rd Edition

Автор: Sanford Weisberg
Название: Applied Linear Regression, 3rd Edition
ISBN: 0471663794 ISBN-13(EAN): 9780471663799
Издательство: Wiley
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Цена: 95570.00 T
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Описание: Applied Linear Regression, Third Edition is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this Third Edition stresses using graphical methods to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. This text is an excellent tool f learning how to use linear regression analysis techniques to solve and gain insight into real--life problems.


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