Multivariate Data AnalysisKEY BENEFIT: For over 30 years, this text has provided students 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 students how to understand and make use of the results of specific statistical techniques. In this seventh revision, 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. Preparing For a MV Analysis; Dependence Techniques; Interdependence Techniques; Moving Beyond the Basic Techniques MARKET: Statistics and statistical research can provide managers with invaluable data. This textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace. |
From inside the book
Results 1-3 of 97
Page 82
... variables . Two variables ( X6 and X16 ) were transformed by taking the square root . X7 was transformed by ... nonmetric variable . For our purposes , we examine each of the metric variables across the five nonmetric variables in the ...
... variables . Two variables ( X6 and X16 ) were transformed by taking the square root . X7 was transformed by ... nonmetric variable . For our purposes , we examine each of the metric variables across the five nonmetric variables in the ...
Page 86
... variables , this linearity relates to the patterns of association between each pair of variables and the ability of the correlation ... Variables with Dummy Variables Nonmetric Variable 86 Section I⚫ Preparing to Apply Multivariate Analysis.
... variables , this linearity relates to the patterns of association between each pair of variables and the ability of the correlation ... Variables with Dummy Variables Nonmetric Variable 86 Section I⚫ Preparing to Apply Multivariate Analysis.
Page 101
... variables included in the analysis . A logical question at this point would ... Nonmetric variables , however , are more problematic because they cannot use ... Factor Analysis 101 Variable Selection and Measurement Issues.
... variables included in the analysis . A logical question at this point would ... Nonmetric variables , however , are more problematic because they cannot use ... Factor Analysis 101 Variable Selection and Measurement Issues.
Contents
Overview of Multivariate Methods | 2 |
Overview of Multivariate Methods | 4 |
Preparing to Apply Multivariate Analysis | 31 |
Copyright | |
28 other sections not shown
Other editions - View all
Multivariate Data Analysis Joseph Hair,Rolph Anderson,Bill Black,Barry Babin No preview available - 2016 |
Multivariate Data Analysis Joseph F. Hair (Jr),William C. Black,Barry J. Babin,Rolph E. Anderson No preview available - 2013 |
Common terms and phrases
application approach assess assumptions attributes bivariate calculated Chapter classification cluster analysis cluster solution combination comparison concept conjoint analysis correspondence analysis covariates customers defined deletion dependent measures dimensions discriminant analysis discriminant function discussed distribution dummy variables effect equation error evaluate examine example factor analysis factor loadings factor matrix factor scores HBAT holdout sample homoscedasticity identify impact independent variables indicate individual interaction interpretation linear logistic regression MANOVA Marketing means methods metric variables missing data process multicollinearity multiple regression multivariate analysis multivariate techniques nonmetric variables normality number of factors objects observations outliers pattern percent perceptual map predictive accuracy preference procedure profiles regression analysis regression coefficients regression model relationship represent researcher respondents rotation sample sizes selected set of variables similar specific Stage statistical power statistical significance statistical tests stepwise structure summated scale Table tion transformations Type I error univariate V₁ valid values variable(s variate versus X₁ X6 Product Quality