Handbook of regression modeling in people analytics, Mcnulty, Keith
Автор: A. Wojtkiewicz Roger Название: Elementary Regression Modeling ISBN: 1506303471 ISBN-13(EAN): 9781506303475 Издательство: Sage Publications Рейтинг: Цена: 88710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This user-friendly text builds on simple differences between groups to explain regression and regression modeling and provides a conceptual basis for the processes and procedures researchers follow when conducting regression analyses.
Автор: Knafl Название: Adaptive Regression for Modeling Nonlinear Relationships ISBN: 3319339443 ISBN-13(EAN): 9783319339443 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes.
Автор: Ezra Hauer Название: The Art of Regression Modeling in Road Safety ISBN: 3319125281 ISBN-13(EAN): 9783319125282 Издательство: Springer Рейтинг: Цена: 113190.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Frank E. Harrell , Jr. Название: Regression Modeling Strategies ISBN: 331933039X ISBN-13(EAN): 9783319330396 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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 Рейтинг: Цена: 83810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Adam G. Petrie Название: Introduction to Regression and Modeling with R ISBN: 1631892509 ISBN-13(EAN): 9781631892509 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 89410.00 T Наличие на складе: Невозможна поставка. Описание: The primary goal of Introduction to Regression Modeling with R is to help students understand the types of questions regression models can and cannot answer and how to answer them. Students will become familiar with fitting both simple and complex models using R. The text stresses model interpretation rather than tedious algebra or mathematics since R calculates almost all relevant numbers. Equations are presented to help flesh out the ""why"" behind various aspects of modeling and to provide additional insights into how regression models work.Introduction to Regression Modeling with R includes numerous examples and sections not featured in earlier editions, and R code is visible throughout the book. A custom R package accompanies the text and was written to aid in regression model building and interpretation. This text is intended for those studying for professions that make use of large and potentially complex datasets arising observational studies, particularly in the fields of business and social science.
Автор: Chatterjee Samprit, Simonoff Jeffrey S. Название: Handbook of Regression Analysis with Applications in R ISBN: 1119392373 ISBN-13(EAN): 9781119392378 Издательство: Wiley Рейтинг: Цена: 117160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Handbook and reference guide for students and practitioners of statistical regression-based analyses in R
Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data.
The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include:
In the new edition of the Handbook, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website.
Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.
Автор: Knafl George J., Ding Kai Название: Adaptive Regression for Modeling Nonlinear Relationships ISBN: 3319816381 ISBN-13(EAN): 9783319816388 Издательство: Springer Рейтинг: Цена: 74830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes.
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