Applied Linear Regression, 3rd Edition, Sanford Weisberg
Автор: Weisberg Sanford Название: Applied Linear Regression ISBN: 1118386086 ISBN-13(EAN): 9781118386088 Издательство: Wiley Цена: 12821 р. Наличие на складе: Поставка под заказ. 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.
Автор: David W. Hosmer Jr.,Stanley Lemeshow,Rodney X. Stu Название: Applied Logistic Regression ISBN: 0470582472 ISBN-13(EAN): 9780470582473 Издательство: Wiley Рейтинг: Цена: 12010 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A new edition of the definitive guide to logistic regression modeling for health science and other applications Praise for the Second Edition ". . . an excellent book that balances many objectives well. . . . Applied Logistic Regression is an ideal
Автор: George A. F. Seber Название: Linear Regression Analysis, 2nd Edition ISBN: 0471415405 ISBN-13(EAN): 9780471415404 Издательство: Wiley Рейтинг: Цена: 17210 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methodsmodel fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested.
Описание: This book introduces several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. The second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subject and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure. He is the author of numerous technical articles and several books and he is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Also Available: Christensen, Ronald. Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition (1996). New York: Springer-Verlag New York, Inc. Christensen, Ronald. Log-Linear Models and Logistic Regression, Second Edition (1997). New York: Springer-Verlag New York, Inc.
Описание: Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
Автор: Kutner Название: Applied Linear Regression Models ISBN: 0071289356 ISBN-13(EAN): 9780071289351 Издательство: McGraw-Hill Рейтинг: Цена: 6236 р. Наличие на складе: Поставка под заказ.
Описание: The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. More detailed consideration of grouped as opposed to case-wise data throughout the book Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data Updated coverage of unordered and ordered polytomous logistic regression models.
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.
Описание: A comprehensive and thoroughly up-to-date look at regression analys-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of: * Indicator variables, making the connection between regression and analysis-of-variance modelss * Variable selection and model-building techniques * The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures * Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation * Generalized linear models The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site.
Автор: Rawlings Название: Applied Regression Analysis ISBN: 0387984542 ISBN-13(EAN): 9780387984544 Издательство: Springer Рейтинг: Цена: 10027 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving
optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least
squares as an effective research tool.
"Applied Regression Analysis" is aimed at the
scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods
without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course
to graduate students.
"Applied Regression Analysis" serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It
also provides a bridge between a two-semester introduction to statistical methods and a theoretical linear models course. "Applied Regression Analysis" emphasizes the concepts and
the analysis of data sets.
It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting
regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models
are also discussed.
Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the
Автор: John Fox and Sanford Weisberg Название: An R Companion to Applied Regression ISBN: 141297514X ISBN-13(EAN): 9781412975148 Издательство: Sage Publications Рейтинг: Цена: 8973 р. Наличие на складе: Поставка под заказ.
Описание: The authors provide a step-by-step guide to using the high-quality free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, enhanced coverage of R graphics and programming, and substantial web-based support materials.
Автор: Lewis-Beck Michael S. Professor, Lewis-Beck Colin Название: Applied Regression: An Introduction ISBN: 1483381471 ISBN-13(EAN): 9781483381473 Издательство: Sage Publications Рейтинг: Цена: 2645 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Known for its readability and clarity, this Second Edition provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.
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