Linear Regression Models: Applications in R, Hoffmann John P.

Автор: Hocking Ronald R
Название: Methods and Applications of Linear Models
ISBN: 1118329503 ISBN-13(EAN): 9781118329504
Издательство: Wiley
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Описание: Praise for the Second Edition "An essential desktop reference book... it should definitely be on your bookshelf.

Автор: Eric Vittinghoff; David V. Glidden; Stephen C. Shi
Название: Regression Methods in Biostatistics
ISBN: 1489998543 ISBN-13(EAN): 9781489998545
Издательство: Springer
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Цена: 11878.00 р.
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Описание: This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

Автор: Vittinghoff
Название: Regression Methods in Biostatistics
ISBN: 0387202757 ISBN-13(EAN): 9780387202754
Издательство: Springer
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Цена: 11313.00 р.
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Описание: An introduction to the multipredictor regression methods widely used in biostatistics. This book covers 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 linear models for counts and other outcomes.

Автор: Sengupta Debasis, Jammalamadaka S. Rao
Название: Linear Models and Regression with R: An Integrated Approach
ISBN: 9811229287 ISBN-13(EAN): 9789811229282
Издательство: World Scientific Publishing
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Описание: Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices.

Название: Regression Modeling Strategies
ISBN: 3319194240 ISBN-13(EAN): 9783319194240
Издательство: Springer
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Цена: 12577.00 р.
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Описание: Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model.

Автор: Zelterman, Daniel (yale University, Connecticut)
Название: Regression for health and social science
ISBN: 1108478182 ISBN-13(EAN): 9781108478182
Издательство: Cambridge Academ
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Цена: 7918.00 р.
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Описание: Using every-day examples and numerous exercises, this text covers the basics of linear models with a minimum of mathematics. The emphasis is on issues involved in the analysis and the interpretation of computer output. R code is provided and explained allowing readers to apply the methods to their own data.

Автор: Molloy Graham
Название: Regression Analysis and Linear Models
ISBN: 1635497051 ISBN-13(EAN): 9781635497052
Издательство: Неизвестно
Цена: 24461.00 р.
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Автор: Hoffmann John P.
Название: Linear Regression Models: Applications in R
ISBN: 0367753669 ISBN-13(EAN): 9780367753665
Издательство: Taylor&Francis
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Цена: 11176.00 р.
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Описание: This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.

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: 25027.00 р.
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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.

Автор: Frank E. Harrell , Jr.
Название: Regression Modeling Strategies
ISBN: 331933039X ISBN-13(EAN): 9783319330396
Издательство: Springer
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Цена: 10480.00 р.
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Описание: Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model.

Автор: Frank E. Harrell
Название: Regression Modeling Strategies
ISBN: 1441929185 ISBN-13(EAN): 9781441929181
Издательство: Springer
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Цена: 12571.00 р.
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Описание: There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield ..".students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks ) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.

Автор: Jammalamadaka S Rao, Sengupta Debasis
Название: Linear Models And Regression With R: An Integrated Approach
ISBN: 9811200408 ISBN-13(EAN): 9789811200403
Издательство: World Scientific Publishing
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Цена: от 6763.00 р.
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Описание:

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed.

The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.

This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:

• Applications with data sets, and their implementation in R,
• Comprehensive coverage of regression diagnostics and model building,
• Coverage of other special topics such as collinearity, stochastic and inequality constraints, misspecified models, etc.,
• Use of simple statistical ideas and interpretations to explain advanced concepts, and simpler proofs of many known results,
• Discussion of models covering mixed-effects/variance components, spatial, and time series data with partially unknown dispersion matrix,
• Thorough treatment of the singular linear model, including the case of multivariate response,
• Insight into updates in the linear model, and their connection with diagnostics, design, variable selection, Kalman filter, etc.,
• Extensive discussion of the foundations of linear inference, along with linear alternatives to least squares.