Increasingly, researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory and neglect to explain how to perform and interpret multivariate analyses on real-life data. For years, Afifi and Clark's Computer-Aided Multivariate Analysis has been a welcome exception-helping researchers choose the appropriate analyses for their data, carry them out, and interpret the results. Only a limited knowledge of statistics is assumed, and geometrical and graphical explanations are used to explain what the analyses do. However, the basic model is always given, and assumptions are discussed. Reflecting the increased emphasis on computers, the Third Edition includes three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis. Another new chapter focuses on log-linear analysis of multi-way frequency tables. Students in a wide range of fields-ranging from psychology, sociology, and physical sciences to public health and biomedical science-will find Computer-Aided Multivariate Analysis especially informative and enlightening.
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This book has been written for investigators, specifically behavioral scientists, biomedical scientists, industrial or academic researchers who wish to perform multivariate statistical analyses on their data and understand the results. lt has been written so that it can either be used as a self-guided textbook or as a text in an applied course in multivariate analysis. In addition, we believe that the book will be helpful to many statisticians who have been trained in conventional mathematical statistics who are now working as statistical consultants and need to give explanations to clients who Iack sufficient background in mathematics. We do not present mathematical derivations ofthe techniques in this book; rather we rely on geometric and graphical arguments and on examples to illustrate them. The mathematicallevel has been kept deliberately low, with no mathematics beyond the high-school Ievel required. The derivations of the techniques are referenced. The original derivations for most of the current techniques were done 50 years ago so we feel that the applications of these techniques to real-life problems is the 'fun' part now. We have assumed that the reader has taken a basic course in statistics that includes test of hypotheses. Many computer programs use analysis of variance and that part of the results of the program can only be understood if the reader is familiar with one-way analysis of variance.
This text helps researchers choose the appropriate analysis for their data, carry out the research, and interpret the results. Only a limited knowledge of statistics is assumed, and geometric and graphical explanations are used to explain what the analyses do. But the basic model is always given and assumptions are discussed. The computer emphasis is enhanced by the inclusion of three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis.
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