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179,970원  | 20250326  | 9781032583983

Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has expanded since the publication of the second edition.

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0원  | 20250523  | 9781584887584

Generalized Linear Models: Theory and Applications provides a comprehensive, practical introduction to generalized linear models that covers all of the main models and methods of estimation. Worked examples of real data are backed up by implementation in a range of software packages, including R, Stata, SAS, and LogiXact. The examples presented are taken predominantly from the health and social sciences, including health outcomes research, genetics, economics, education, and psychology. Material is supported by a website with data sets, software links, and further examples.

Shayle R. Searle, Marvin H. J. Gruber  | Wiley

58,000원  | 20170601  | 9781118952832

Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book more reader friendly than the first edition.

 | Springer Nature B.V.

74,480원  | 20221116  | 9783031081774

This book is designed as a textbook for graduate students and as a resource for researchers seeking a thorough mathematical treatment of its subject. It develops the main results of regression and the analysis of variance, as well as the central results on confounded and fractional factorial experiments. Matrix theory is deemphasized; its role is taken instead by the theory of linear transformations between vector spaces.

 | Springer

121,040원  | 20220225  | 9783030641696

Designed to develop fluency with the underlying mathematics and to build a deep understanding of the principles, it's an excellent basis for a one-semester course on statistical theory and linear modeling for intermediate undergraduates or graduate students. Three chapters gradually develop the essentials of linear model theory.

 | Lightning Source

292,500원  | 20210101  | 9780849396717

This text stands apart from other books on experiment design. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general the...

 | Springer

167,600원  | 20250502  | 9789819647255

Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models.

Bernard Schutz  | Cambridge University Press

70,000원  | 20220630  | 9781108492676

Updating one of the most widely used introductory textbooks on Einstein's general relativity, this third edition includes the latest updates on gravitational waves, black holes, and cosmology. It introduces the science of relativity to final-year undergraduates and graduate students, requiring only a minimal background in mathematics.

 | Springer Nature B.V.

74,480원  | 20210227  | 9783030641689

This book provides a visual and intuitive coverage of the core theory of linear models. Designed to develop fluency with the underlying mathematics and to build a deep understanding of the principles, it's an excellent basis for a one-semester course on statistical theory and linear modeling for intermediate undergraduates or graduate students. Three chapters gradually develop the essentials of linear model theory.

W. D. Brinda  | Springer

167,600원  | 20210205  | 9783030641665

Designed to develop fluency with the underlying mathematics and to build a deep understanding of the principles, it's an excellent basis for a one-semester course on statistical theory and linear modeling for intermediate undergraduates or graduate students. Three chapters gradually develop the essentials of linear model theory.

Julian J. Faraway  | CRC Press

202,470원  | 20150701  | 9781439887332

A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications.

Clarke, Brenton R.  | Wiley

201,590원  | 20080804  | 9780470025666

This book represents a serious approach to the study of linear models and their applications through discussion of analysis of variance (ANOVA) techniques. Throughout the book there is an equal emphasis on orthogonal representations that delve from the history of the subject and on the vector-matrix approach that has surfaced in recent years.

Zelterman, Daniel  | Cambridge

169,460원  | 20210101  | 9780521761598

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and de...

Monahan, John F.  | CRC

139,470원  | 20100403  | 9781420062014

Employing non-full-rank design matrices throughout, this book enables understanding of basic linear models. This book introduces the basic algebra and geometry of the linear least squares problem, before delving into estimability and the Gauss-Markov model.

Zimmerman, Dale L., Ver Hoef, Jay M.  | CRC Press

184,470원  | 20240417  | 9780367183349

Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data.