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Author: James M. Lattin
Publisher: Duxbury Press
ISBN: 9780534349745
Category : Mathematics
Languages : en
Pages : 556
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Book Description
Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Carroll, and Green have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not advanced statistics majors. The text provides a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through applications. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with vectors and matrix algebra. Additionally, each chapter follows a standard format. This format begins by discussing a general set of research objectives, followed by illustrative examples of problems in different areas. Then it provides an explanation of how each method works, followed by a sample problem, application of the technique, and interpretation of results.
Author: James M. Lattin
Publisher: Duxbury Press
ISBN: 9780534349745
Category : Mathematics
Languages : en
Pages : 556
Get Book
Book Description
Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Carroll, and Green have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not advanced statistics majors. The text provides a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through applications. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with vectors and matrix algebra. Additionally, each chapter follows a standard format. This format begins by discussing a general set of research objectives, followed by illustrative examples of problems in different areas. Then it provides an explanation of how each method works, followed by a sample problem, application of the technique, and interpretation of results.
Author: John Spicer
Publisher: SAGE
ISBN: 9781412904018
Category : Mathematics
Languages : en
Pages : 256
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Book Description
A short introduction to the subject, this text is aimed at students & practitioners in the behavioural & social sciences. It offers a conceptual overview of the foundations of MDA & of a range of specific techniques including multiple regression, logistic regression & log-linear analysis.
Author: Joseph Hair
Publisher: Pearson Higher Ed
ISBN: 0133792684
Category : Business & Economics
Languages : en
Pages : 816
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Book Description
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For graduate and upper-level undergraduate marketing research courses. For over 30 years, Multivariate Data Analysis has provided readers with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to readers how to understand and make use of the results of specific statistical techniques. In this Seventh Edition, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
Author: Brian Everitt
Publisher: Springer Science & Business Media
ISBN: 1441996508
Category : Mathematics
Languages : en
Pages : 284
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Book Description
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.
Author: Norman Cliff
Publisher:
ISBN:
Category : Multivariate analysis
Languages : en
Pages : 536
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Book Description
Author: Abdelmonem Afifi
Publisher: CRC Press
ISBN: 1351788906
Category : Mathematics
Languages : en
Pages : 528
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Book Description
This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computing, and exercises. Updated with a new chapter on data visualization.
Author: Paul E. Green
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 546
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Book Description
Single criterion, multiple predictor association; Multiple criterion, multiple predictor association; The analysis of interdependence.
Author: Michel Jambu
Publisher: Elsevier
ISBN: 0080923674
Category : Mathematics
Languages : en
Pages : 432
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Book Description
With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques. Key Features * Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones * Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples * Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques
Author: Alan J. Izenman
Publisher: Springer Science & Business Media
ISBN: 0387781897
Category : Mathematics
Languages : en
Pages : 757
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Book Description
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Author: Klaus Backhaus
Publisher: Springer Nature
ISBN: 3658404116
Category : Business & Economics
Languages : en
Pages : 618
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Book Description
Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS. Extensions of the methods and links to other procedures are discussed and recommendations for application are given. An introductory chapter presents the basic ideas of the multivariate methods covered in the book and refreshes statistical basics which are relevant to all methods. For the 2nd edition, all chapters were checked and calculated using the current version of IBM SPSS. Contents Introduction to empirical data analysis Regression analysis Analysis of variance Discriminant analysis Logistic regression Contingency analysis Factor analysis Cluster analysis Conjoint analysis The original German version is now available in its 17th edition. In 2015, this book was honored by the Federal Association of German Market and Social Researchers as “the textbook that has shaped market research and practice in German-speaking countries”. A Chinese version is available in its 3rd edition. On the website www.multivariate-methods.info, the authors further analyze the data with Excel and R and provide additional material to facilitate the understanding of the different multivariate methods. In addition, interactive flashcards are available to the reader for reviewing selected focal points. Download the Springer Nature Flashcards App and use exclusive content to test your knowledge.
