Multivariate Data Analysis
Prentice Hall, 2010 - Business & Economics - 785 pages
KEY 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.
82 pages matching structure in this book
Results 1-3 of 82
What people are saying - Write a review
We haven't found any reviews in the usual places.
Overview of Multivariate Methods
39 other sections not shown
Other editions - View all
application approach assess assumptions attributes bivariate calculated Chapter classiﬁcation cluster analysis cluster solution combination comparison concept conjoint analysis correlation correspondence analysis covariates customers deﬁned dimensions discriminant analysis discriminant function discussed distribution dummy variables effect equation evaluate examine example factor analysis factor loadings ﬁnal ﬁnd ﬁrms ﬁrst ﬁve HBAT holdout sample homoscedasticity identiﬁed identify impact independent variables indicate individual interaction interpretation linear logistic regression MANOVA Marketing means methods metric variables missing data process multicollinearity multiple discriminant analysis multiple regression multivariate analysis multivariate techniques nonmetric variables normality number of factors number of variables objects observations outliers pattem percent perceptual map preference procedure proﬁles reﬂect regression analysis regression coefﬁcients regression model relationship represent researcher respondents rotation sample sizes scores selected signiﬁcance level similar speciﬁc Stage statistical power statistical signiﬁcance statistical tests stepwise structure summated scale Table tion transformations Type I error univariate valid values variable’s variate versus