Cover image for Applied multivariate data analysis/ h {disket}
Title:
Applied multivariate data analysis/ h {disket}
Personal Author:
Publication Information:
New York: Springer-Verlag, 1991
Physical Description:
2 Computer disk 5 1/4 in
ISBN:
9780387978048

9783540978046
General Note:
Accompanies text with same title

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30000001619901 DSK 375 Open Access Computer File Diskette (Open Shelves)
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30000001691371 DSK 375 Open Access Computer File Diskette (Open Shelves)
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Summary

Summary

A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de­ sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec­ ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta.


Reviews 1

Choice Review

This is the second of two volumes designed for doctoral students in business. As in the first volume (1991), Jobson assumes familiarity with basic concepts from undergraduate statistics, matrix algebra, and calculus. The first chapter discusses assumptions, models, and statistical analyses for contingency tables. The second chapter treats multivariate normal samples. Included are geometric interpretations for data matrices, tests for normality and outliers, robust estimation, and many of the classical techniques for making inferences about location, scale, and correlation parameters. The third chapter discusses multivariate analysis of variance, discriminant analysis, and techniques for analyzing regression type data with qualitative response values. The fourth introduces the broad area of principal components, factor, and correspondence analysis. The final chapter is on cluster analysis and multidimensional scaling. The general pattern throughout is to present a brief summary of the theoretical concepts and then give detailed numerical examples with references to the major computing packages. This book will appeal to students in statistics, business, and science. Advanced undergraduate through professional. F. Giesbrecht; North Carolina State University