Applied Multivariate Statistical Analysis And Related Topics With R
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Applied Multivariate Statistical Analysis And Related Topics With R
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Author : Lang WU
language : en
Publisher: EDP Sciences
Release Date : 2021-04-27
Applied Multivariate Statistical Analysis And Related Topics With R written by Lang WU and has been published by EDP Sciences this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Mathematics categories.
Multivariate analysis is a popular area in statistics and data science. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly used multivariate analysis models and methods in practice.
Applied Multivariate Statistics With R
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Author : Daniel Zelterman
language : en
Publisher: Springer
Release Date : 2015-08-03
Applied Multivariate Statistics With R written by Daniel Zelterman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-03 with Medical categories.
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.
An Introduction To Applied Multivariate Analysis With R
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Author : Brian Everitt
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-23
An Introduction To Applied Multivariate Analysis With R written by Brian Everitt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-23 with Mathematics categories.
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Applied Multivariate Statistical Concepts
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Author : DEBBIE L. HAHS-VAUGHN
language : en
Publisher: Routledge
Release Date : 2024-10-29
Applied Multivariate Statistical Concepts written by DEBBIE L. HAHS-VAUGHN and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Business & Economics categories.
This second edition of Applied Multivariate Statistical Concepts covers the classic and cutting-edge multivariate techniques used in today's research. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader's master key concepts so they can implement and interpret results generated by today's sophisticated software. Additional features include examples using real data from the social sciences; templates for writing research questions and results that provide manuscript-ready models; step-by-step instructions on using R and SPSS statistical software with screenshots and annotated output; clear coverage of assumptions including how to test them and the effects of their violation; and conceptual, computational, and interpretative example problems that mirror the real-world problems students encounter in their studies and careers. This edition features expanded coverage of topics such as propensity score analysis; path analysis and confirmatory factor analysis; centering, moderation effects, and power as related to multilevel modelling. New topics are introduced such as addressing missing data and latent class analysis, while each chapter features an introduction to using R statistical software. This textbook is ideal for courses on multivariate statistics/analysis/design, advanced statistics and quantitative techniques, as well as for graduate students broadly in social sciences, education and behavioral sciences. It also appeals to researchers with no training in multivariate methods.
Applied Multivariate Statistical Analysis Classic Version
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Author : Richard A. Johnson
language : en
Publisher: Pearson
Release Date : 2018-03-18
Applied Multivariate Statistical Analysis Classic Version written by Richard A. Johnson and has been published by Pearson this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-18 with Multivariate analysis categories.
This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
An Introduction To Applied Multivariate Analysis With R
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Author : Brian Everitt
language : en
Publisher: Springer
Release Date : 2011-05-03
An Introduction To Applied Multivariate Analysis With R written by Brian Everitt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-03 with Mathematics categories.
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Applied Multivariate Statistical Analysis
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Author : Richard Arnold Johnson
language : en
Publisher:
Release Date : 2002
Applied Multivariate Statistical Analysis written by Richard Arnold Johnson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Multivariate analysis categories.
Topics In Applied Multivariate Analysis
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Author : D. M. Hawkins
language : en
Publisher: Cambridge University Press
Release Date : 1982-04-22
Topics In Applied Multivariate Analysis written by D. M. Hawkins and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982-04-22 with Mathematics categories.
Multivariate methods are employed widely in the analysis of experimental data but are poorly understood by those users who are not statisticians. This is because of the wide divergence between the theory and practice of multivariate methods. This book provides concise yet thorough surveys of developments in multivariate statistical analysis and gives statistically sound coverage of the subject. The contributors are all experienced in the theory and practice of multivariate methods and their aim has been to emphasize the major features from the point of view of applicability and to indicate the limitations and conditions of the techniques. Professional statisticians wanting to improve their background in applicable methods, users of high-level statistical methods wanting to improve their background in fundamentals, and graduate students of statistics will all find this volume of value and use.
Applied Multivariate Analysis
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Author : S. James Press
language : en
Publisher:
Release Date : 1972
Applied Multivariate Analysis written by S. James Press and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with Mathematics categories.
Textbook on methodology and model-building techniques of multivariate statistical analysis, with particular reference to applications in the social sciences - includes a bibliography pp. 483 to 509, graphs and statistical tables.
Applied Multivariate Methods For Data Analysts
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Author : Dallas E. Johnson
language : en
Publisher: Duxbury Resource Center
Release Date : 1998
Applied Multivariate Methods For Data Analysts written by Dallas E. Johnson and has been published by Duxbury Resource Center this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Mathematics categories.
Statisticians and nonstatisticians alike will appreciate this modern and comprehensive new book on multivariate statistical methods that utilizes statistical computing packages throughout. Author Dallas Johnson uses real-life examples and explains the "when to," "why to," and "how to" of numerous multivariate methods, stressing the importance and practical application of each. Technical details are kept to a minimum, making the book accessible to readers.