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Multivariate data analysis

By: Joseph F.Hair..[et al.].
Contributor(s): Hair, Joseph F | Babin, Barry J | William, Black C | Anderson, Rolph E.
Material type: materialTypeLabelBookPublisher: Delhi : Cengage Learning, 2019Edition: Eighth edition.Description: xvii, 813 pages : illustrations.ISBN: 9789353501358.Subject(s): Multivariate analysisDDC classification: 519.535
Contents:
1. Overview of multivariate methods -- Section I: Preparing for multivariate analysis -- Chapter 2: Examining your data -- Section II: Interdependence techniques -- Chapter 3: Exploratory factor analysis -- Chapter 4: Cluster analysis -- Section III: Dependence techniques -- metric outcomes -- Chapter 5: Multiple regression analysis -- Chapter 6: MANOVA:extending ANOVA -- Section IV: Dependence techniques -- non-metric outcomes -- Chapter 7: Multiple discriminant analysis -- Chapter 8: Logistic regression: regression with a binary dependent variable -- Section V: Moving beyond the basics -- Chapter 9: Structural equation modeling: an introduction -- Chapter 10: SEM: confirmatory factor analysis -- Chapter 11: Testing structural equation models -- Chapter 12: Advanced SEM topics -- Chapter 13: Partial least squares structural equation modeling (PLS-SEM).
Summary: Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis for the non-statistician. The eighth edition incorporates pivotal advances in technology that will assist students in gaining a firm understanding of statistical and managerial principles so as to develop a "comfort zone" not only for the statistical, but also the practical issues involved.
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Item type Current location Collection Call number Status Date due Barcode Item holds
Lending Books Lending Books Pampaimadu Library
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Reference 519.535 MUL (Browse shelf) Available 015995
Lending Books Lending Books Pampaimadu Library
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Reference 519.535 MUL (Browse shelf) Available 015974
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Revision of: Multivariate data analysis / Joseph F. Hair, Jr. ... [et al.]. c2006. 7th ed.

1. Overview of multivariate methods --
Section I: Preparing for multivariate analysis --
Chapter 2: Examining your data --
Section II: Interdependence techniques --
Chapter 3: Exploratory factor analysis --
Chapter 4: Cluster analysis --
Section III: Dependence techniques --
metric outcomes --
Chapter 5: Multiple regression analysis --
Chapter 6: MANOVA:extending ANOVA --
Section IV: Dependence techniques --
non-metric outcomes --
Chapter 7: Multiple discriminant analysis --
Chapter 8: Logistic regression: regression with a binary dependent variable --
Section V: Moving beyond the basics --
Chapter 9: Structural equation modeling: an introduction --
Chapter 10: SEM: confirmatory factor analysis --
Chapter 11: Testing structural equation models --
Chapter 12: Advanced SEM topics --
Chapter 13: Partial least squares structural equation modeling (PLS-SEM).

Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis for the non-statistician. The eighth edition incorporates pivotal advances in technology that will assist students in gaining a firm understanding of statistical and managerial principles so as to develop a "comfort zone" not only for the statistical, but also the practical issues involved.

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