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Multivariate Analysis, Design of Experiments, and Survey Sampling, Wachowicz, Monica


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Автор: Wachowicz, Monica
Название:  Multivariate Analysis, Design of Experiments, and Survey Sampling
ISBN: 9780367579180
Издательство: Taylor&Francis
Классификация:



ISBN-10: 0367579189
Обложка/Формат: Paperback
Страницы: 688
Вес: 1.27 кг.
Дата издания: 30.06.2020
Серия: Statistics: a series of textbooks and monographs
Язык: English
Размер: 229 x 152
Читательская аудитория: Tertiary education (us: college)
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Поставляется из: Европейский союз
Описание: Describes recent developments and surveys important topics in the areas of multivariate analysis, design of experiments, and survey sampling. Features the work of nearly 50 international leaders.

An Introduction to Multivariate Statistical Analysis, Third Edition

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 184750.00 T
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Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.

Multivariate Analysis for the Behavioral Sciences, Second Edition

Автор: Kimmo Vehkalahti , Brian S. Everitt
Название: Multivariate Analysis for the Behavioral Sciences, Second Edition
ISBN: 0815385153 ISBN-13(EAN): 9780815385158
Издательство: Taylor&Francis
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Цена: 86760.00 T
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Описание: Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists.

Latent Class Analysis of Survey Error

Автор: Biemer
Название: Latent Class Analysis of Survey Error
ISBN: 0470289074 ISBN-13(EAN): 9780470289075
Издательство: Wiley
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Цена: 106600.00 T
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Описание: This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the estimates and make use of the results in reducing the error for future surveys.

Statistical Tables for Multivariate Analysis

Автор: Peter Wadsack; Heinz Kres
Название: Statistical Tables for Multivariate Analysis
ISBN: 1461256119 ISBN-13(EAN): 9781461256113
Издательство: Springer
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Цена: 111790.00 T
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Описание: An English translation of my "Statistisahe Tafeln zur multivariaten Analysis - Ein Handbuah mit Hinweisen zur Anwendung" was planned already in 1975 when I prepared the German volume. The tables were immediately supplied with German and English headings for the inten- ded photo-offset printing. In the meantime. new and important tables for multivariate statistiaal hypotheses and proaedures have been aompiled and published. Only four of them have been inaorporated in the present volume. The seleation of these tables must be on an individual basis for reasons of spaae. Let me mention only the new tables for sample size determination in MANOVA. The instruations for using the tables are all organized in the same way. They are kept short sinae it is not the task of suah a work to provide an introduation to the theory and praatiae of multivariate analysis. I have renounaed giving examples for the same reason. I wish instead to refer the reader to the many good textbooks that are avai- lable. as well as to my own works on methods that are in preparation. Furthermore. I am of the opinion that statistiaal tables should aaaom- pany the textbook rather than be inaluded in it. vii viii Finally, my thanks go to the translator, Mr. Peter R. Wadsack, as ell as to the ladies and gentlemen of Springer-Verlag for their pleasant collaboration and their indulgence of my numerous requests.

Multivariate Statistical Modeling and Data Analysis

Автор: H. Bozdogan; Arjun K. Gupta
Название: Multivariate Statistical Modeling and Data Analysis
ISBN: 9401082642 ISBN-13(EAN): 9789401082648
Издательство: Springer
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Цена: 136910.00 T
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Описание: The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor- relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

Applied Multivariate Analysis

Автор: Ira H. Bernstein; Calvin P. Garbin; Gary K. Teng
Название: Applied Multivariate Analysis
ISBN: 1461387426 ISBN-13(EAN): 9781461387428
Издательство: Springer
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Цена: 97820.00 T
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Описание: Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intel- lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to "concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis . . . . (and) . . . who are more interested in the sciences than in mathematics, among other characteristics. " I find the distinction between individuals who are temperamentally "mathe- maticians" (whom philosophy students might call "Platonists") and "scientists" ("Aristotelians") useful as long as it is not pushed to the point where one assumes "mathematicians" completely disdain data and "scientists" are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the "scientist" rather than the "mathematician" role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions.

Multivariate Statistical Analysis

Автор: V.I. Serdobolskii
Название: Multivariate Statistical Analysis
ISBN: 9048155932 ISBN-13(EAN): 9789048155934
Издательство: Springer
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Цена: 121110.00 T
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Описание: Multivariate Statistical Analysis

Applied Multivariate Data Analysis

Автор: J.D. Jobson
Название: Applied Multivariate Data Analysis
ISBN: 1461269601 ISBN-13(EAN): 9781461269601
Издательство: Springer
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Цена: 46570.00 T
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Описание: An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory.

Innovations in Multivariate Statistical Analysis

Автор: Risto D.H. Heijmans; D.S.G. Pollock; Albert Satorr
Название: Innovations in Multivariate Statistical Analysis
ISBN: 1461370809 ISBN-13(EAN): 9781461370802
Издательство: Springer
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Цена: 139750.00 T
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Описание: The three decades which have followed the publication of Heinz Neudecker`s seminal paper `Some Theorems on Matrix Differentiation with Special Reference to Kronecker Products` in the Journal of the American Statistical Association (1969) have witnessed the growing influence of matrix analysis in many scientific disciplines.

Applied Multivariate Data Analysis

Автор: J.D. Jobson
Название: Applied Multivariate Data Analysis
ISBN: 1461269474 ISBN-13(EAN): 9781461269472
Издательство: Springer
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Цена: 93160.00 T
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Описание: A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software.

Applied Multivariate Analysis

Автор: Neil H. Timm
Название: Applied Multivariate Analysis
ISBN: 1441929630 ISBN-13(EAN): 9781441929631
Издательство: Springer
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Цена: 83850.00 T
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Описание: Univariate statistical analysis is concerned with techniques for the analysis of a single random variable. This book is about applied multivariate analysis. It was written to p- vide students and researchers with an introduction to statistical techniques for the ana- sis of continuous quantitative measurements on several random variables simultaneously. While quantitative measurements may be obtained from any population, the material in this text is primarily concerned with techniques useful for the analysis of continuous obser- tions from multivariate normal populations with linear structure. While several multivariate methods are extensions of univariate procedures, a unique feature of multivariate data an- ysis techniques is their ability to control experimental error at an exact nominal level and to provide information on the covariance structure of the data. These features tend to enhance statistical inference, making multivariate data analysis superior to univariate analysis. While in a previous edition of my textbook on multivariate analysis, I tried to precede a multivariate method with a corresponding univariate procedure when applicable, I have not taken this approach here. Instead, it is assumed that the reader has taken basic courses in multiple linear regression, analysis of variance, and experimental design. While students may be familiar with vector spaces and matrices, important results essential to multivariate analysis are reviewed in Chapter 2. I have avoided the use of calculus in this text.

Longitudinal Analysis: Modeling Within-Person Fluctuation and Change

Автор: Hoffman Lesa
Название: Longitudinal Analysis: Modeling Within-Person Fluctuation and Change
ISBN: 0415876001 ISBN-13(EAN): 9780415876001
Издательство: Taylor&Francis
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Цена: 193950.00 T
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Описание:

Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter.

Written for non-mathematically-oriented readers, this text features:

  • A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data
  • An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data
  • Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers' understanding of the overall model-building process
  • Sample results sections for each example to provide useful templates for published reports
  • Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data

The book opens with the building blocks of longitudinal analysis--general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data.

Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book's accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.



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