Multivariate data analysis with readings

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Key Benefit: Well-suited for the non-statistician, this applications- oriented introduction to multivariate analysis greatly reduces the amount of statistical notation and terminology used while focusing instead on the fundamental concepts that affect the use of specific techniques. Features an innovative and widely applicable six-step framework for each chapter, and the traditionally strong complement of revisited examples used throughout. Key Topics: Includes detailed guidelines on approaches for interpretation of results. Contains readings in each chapter which emphasize the applications of the techniques. Presents step-by-step flowcharts for each technique, illustrating major issues in each and offering a clear graphical portrayal of the decision process used in applying each technique. Includes a new chapter encompassing the issues of handling missing data, testing and resolving statistical assumption issues, and outlier detection. Revised material on factor, MANOVA, multiple analysis regression and canonical correlation.