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Essentials of Multivariate Data Analysis

Neil H. Spencer
€248.00
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applied statistics for health sciences
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Burt Matrix
Canonical Discrimination
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Classical Multidimensional Scaling
Commonly Used Multivariate Techniques
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Correspondence Analysis
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data visualization techniques
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Excel For Basic Analyses
GENERAL KNOWLEDGE AREA
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Graphically Displaying Multivariate Data
inferential statistics
Introductory Topics In Multivariate Statistics
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Linear Discriminant Functions
Mahalanobis Distance
Multidimensional Scaling
multivariate analysis for social science research
Multivariate Methods In Applied Research
Nonhierarchical Clustering
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PAF Analysis
Pooled Covariance Matrix
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principal component analysis
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Quantitative Analysis
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Reduced Correlation Matrix
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Simple ANOVA
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Product details

  • ISBN 9781138462434
  • Weight: 500g
  • Dimensions: 138 x 216mm
  • Publication Date: 13 Nov 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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Description

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research.

Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for those who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book‘s CRC Press web page.

Providing a firm foundation in the most commonly used multivariate techniques, this text helps readers choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab, R, SAS, SPSS, and Stata.
About Neil H. Spencer
Dr. Neil H. Spencer is a reader in applied statistics and director of the Statistical Services and Consultancy Unit at the University of Hertfordshire. His research interests include multilevel models, multivariate methods, statistical computing, multiple testing, and testing for randomness.

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