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Methods of multivariate analysis
ELSEVIER
Journal of Statistical Planning and Inference 59 (1997) 183 184
journal of statistical planning and inference
Book Review Alvin Rencher, Methods of Multivariate Analysis, (John, New York, 1995, $74.95 ISBN 0471571520, xvi + 627 pp.) This text on multivariate analysis is intended for a graduate level applied class, an advanced undergraduate class or as a desk reference for a user of multivariate analysis. The level of the material is similar to other popular multivariate texts such as Johnson and Wichern (1992) or Morrison (1976). The thirteen chapters contain material on c o m m o n multivariate methods: Hotelling's T 2 test, M A N O V A , multivariate regression, discriminant analysis, canonical correlation analysis, principal component analysis, and factor analysis are all described. There is also some discussion on missing values and a lengthy section on the analysis of repeated measures and growth curves analysis. It thus provides ample material for a one semester course. As a desk reference, it is missing chapters on cluster analysis and correspondence analysis. The focus of the text is on applied multivariate methods and the author has undertaken the task of creating a text readable by a wide audience. Although, l will still get complaints about the number of equations in the text from non-majors, I believe this has been accomplished. There are an ample number of examples throughout the text. Although no actual computer code or output is given in the text, the orientation is toward analysis using the Statistical Analysis System (SAS) and the coverage of material more or less parallels SAS procedures. For example, the chapter on group separation describes canonical variate analysis and stepwise selection of variables. Particularly useful is the section on interpretation of canonical coefficients (raw and standardized) and correlations between coefficients and variables. The section connects well with output from the SAS procedures C A N D I S C and STEPDISC. Although the text is most useful in conjunction with SAS, it can also be used with SPSS or other packages. There are a sufficient number of problems at the end of each chapter. These are a mix of matrix algebra problems, derivations and analysis of data. The solutions to all the problems are given in the end of the book. From a teaching perspective, I would have preferred solutions to just the odd or even numbered problems. The book came with a disk containing many of the data sets used in the homework problems and SAS codes for the examples. While it is certainly advantageous to have these, two features are worth noting. First, the SAS programs are written for a mainframe computer. For those of us who try to use SAS-PC in the classroom, two changes 0378-3758/97/$17.00 ,~:) 1997 Elsevier Science B.V. All rights reserved Pit S03 78- 37 58(96)00098-5
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E.P. Smith/Journal of Statistical Planning and Inference 59 (1997) 183-184
are required. The infile statement needs to be altered and an ENDSAS statement needs to be removed. Second, I would have appreciated some comment lines to help the student understand why certain code was used. Further, it would have been useful if the data sets contained information about the variables and some background on the problem. This information is in the text, however, it would be nice if it were as the data file as well. Teaching applied multivariate methods is more difficult than other courses because of the diverse background of students, difficulties with computer packages and lack of readable texts. I am looking forward to using this text in my multivariate classes as it is a readable text, comes with data sets and examples on a diskette and should appeal to different levels of students.
References Johnson, R.A. and D.W. Wichern (1992). Applied Multivariate Statistical Analysis. Prentice Hall, Englewood Cliffs, NJ. Morrison, D.G. (1976). Multivariate Statistical Methods, 2nd ed. McGraw-Hill, New York.
Eric P. Smith
Virginia Polytechnic Institute and State University
Journal of Statistical Planning and Inference 59 (1997) 183 184
journal of statistical planning and inference
Book Review Alvin Rencher, Methods of Multivariate Analysis, (John, New York, 1995, $74.95 ISBN 0471571520, xvi + 627 pp.) This text on multivariate analysis is intended for a graduate level applied class, an advanced undergraduate class or as a desk reference for a user of multivariate analysis. The level of the material is similar to other popular multivariate texts such as Johnson and Wichern (1992) or Morrison (1976). The thirteen chapters contain material on c o m m o n multivariate methods: Hotelling's T 2 test, M A N O V A , multivariate regression, discriminant analysis, canonical correlation analysis, principal component analysis, and factor analysis are all described. There is also some discussion on missing values and a lengthy section on the analysis of repeated measures and growth curves analysis. It thus provides ample material for a one semester course. As a desk reference, it is missing chapters on cluster analysis and correspondence analysis. The focus of the text is on applied multivariate methods and the author has undertaken the task of creating a text readable by a wide audience. Although, l will still get complaints about the number of equations in the text from non-majors, I believe this has been accomplished. There are an ample number of examples throughout the text. Although no actual computer code or output is given in the text, the orientation is toward analysis using the Statistical Analysis System (SAS) and the coverage of material more or less parallels SAS procedures. For example, the chapter on group separation describes canonical variate analysis and stepwise selection of variables. Particularly useful is the section on interpretation of canonical coefficients (raw and standardized) and correlations between coefficients and variables. The section connects well with output from the SAS procedures C A N D I S C and STEPDISC. Although the text is most useful in conjunction with SAS, it can also be used with SPSS or other packages. There are a sufficient number of problems at the end of each chapter. These are a mix of matrix algebra problems, derivations and analysis of data. The solutions to all the problems are given in the end of the book. From a teaching perspective, I would have preferred solutions to just the odd or even numbered problems. The book came with a disk containing many of the data sets used in the homework problems and SAS codes for the examples. While it is certainly advantageous to have these, two features are worth noting. First, the SAS programs are written for a mainframe computer. For those of us who try to use SAS-PC in the classroom, two changes 0378-3758/97/$17.00 ,~:) 1997 Elsevier Science B.V. All rights reserved Pit S03 78- 37 58(96)00098-5
184
E.P. Smith/Journal of Statistical Planning and Inference 59 (1997) 183-184
are required. The infile statement needs to be altered and an ENDSAS statement needs to be removed. Second, I would have appreciated some comment lines to help the student understand why certain code was used. Further, it would have been useful if the data sets contained information about the variables and some background on the problem. This information is in the text, however, it would be nice if it were as the data file as well. Teaching applied multivariate methods is more difficult than other courses because of the diverse background of students, difficulties with computer packages and lack of readable texts. I am looking forward to using this text in my multivariate classes as it is a readable text, comes with data sets and examples on a diskette and should appeal to different levels of students.
References Johnson, R.A. and D.W. Wichern (1992). Applied Multivariate Statistical Analysis. Prentice Hall, Englewood Cliffs, NJ. Morrison, D.G. (1976). Multivariate Statistical Methods, 2nd ed. McGraw-Hill, New York.
Eric P. Smith
Virginia Polytechnic Institute and State University