Description
Essentials of Multivariate Data Analysis
Author: Spencer Neil H.
Language: English
Subject for Essentials of Multivariate Data Analysis:
Keywords
Occasional Smoker; Diastolic Blood Pressure; multivariate methods in applied research; Systolic Blood Pressure; introductory topics in multivariate statistics; Multidimensional Scaling; quantitative analysis; Pooled Covariance Matrix; graphically displaying multivariate data; Covariance Matrices; commonly used multivariate techniques; Scree Plot; Excel for basic analyses; Age Group; Current Smoker; Linear Discriminant Functions; Case Identifiers; Classical Multidimensional Scaling; Nv Alu; Pulse Rate; Burt Matrix; PAF Analysis; UK Adult Population; Mahalanobis Distance; GENERAL KNOWLEDGE AREA; Simple ANOVA; General Knowledge Scores; Nonhierarchical Clustering; Reduced Correlation Matrix; Correspondence Analysis; Canonical Discrimination
Publication date: 02-2014
Support: Print on demand
Publication date: 11-2017
· 13.8x21.6 cm · Hardback
Description
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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.
