Multivariate data analysis

By: Joseph F.Hair..[et al.]Contributor(s): Hair, Joseph F | Babin, Barry J | William, Black C | Anderson, Rolph EMaterial type: TextTextPublication details: Delhi : Cengage Learning, 2019Edition: Eighth editionDescription: xvii, 813 pages : illustrationsISBN: 9789353501358Subject(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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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Lending Books Lending Books Main Library
Stacks 		Reference 519.535 MUL (Browse shelf(Opens below)) Available 015995
Lending Books Lending Books Main Library
Stacks 		Reference 519.535 MUL (Browse shelf(Opens below)) 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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