Multivariate Data AnalysisPrentice Hall, 2010 - 785 halaman 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. |
Isi
Overview of Multivariate Methods | 1 |
Chapter | 11 |
Chapter | 13 |
Hak Cipta | |
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Edisi yang lain - Lihat semua
Multivariate Data Analysis Joseph Hair,Rolph Anderson,Bill Black,Barry Babin Pratinjau tidak tersedia - 2016 |
Multivariate Data Analysis Joseph F. Hair (Jr),William C. Black,Barry J. Babin,Rolph E. Anderson Pratinjau tidak tersedia - 2013 |
Istilah dan frasa umum
application approach assess assumptions attributes bivariate calculated Chapter classification cluster analysis cluster solution combination comparison concept conjoint analysis correspondence analysis 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 overall pattern percent perceptual map predictive accuracy preference procedure profiles regression analysis regression coefficients regression model relationship represent researcher respondents rotation 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