Statistical Analysis of Management Data

• Κύριος συγγραφέας: Gatignon, Hubert (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York, 2010.
• Έκδοση: 2.
• Θέματα: Statistics.; Econometrics.; Statistics for Business/Economics/Mathematical Finance/Insurance.; Statistical Theory and Methods.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

 Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software. 

 Multivariate Normal Distribution -- Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis -- Confirmatory Factor Analysis -- Multiple Regression with a Single Dependent Variable -- System of Equations -- Canonical Correlation Analysis -- Categorical Dependent Variables -- Rank-Ordered Data -- Error in Variables – Analysis of Covariance Structure -- Cluster Analysis -- Analysis of Similarity and Preference Data -- Appendices.