Get up-to-speed on the latest methods of multivariate statisticsMultivariate statistical methods provide a powerful tool for analyzing data when observations are taken over a period of time on the same subject. With the advent of fast and efficient computers and the availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within reach of most researchers and data analysts. With an emphasis on computing techniques in combination with a full understanding of the mathematics behind the methods, Methods of Multivariate Statistics offers an up-to-date account of multivariate methods. Focusing on the maximum likelihood method for estimation, testing of hypotheses, and "profile analysis," this book offers comprehensive discussions of commonly encountered multivariate data and also covers some practical and important problems lacking in other texts. These include:* Missing at-random observations* "Growth Curve Models" and multivariate one-sided tests applicable in pharmaceutical and medical trials* Bootstrap methods* Principal component method for predicting a multivariate response vector* Outlier detection and handling inference when covariance is singularWith clear chapter introductions and numerous problem sets, Methods of Multivariate Statistics meets every statistician's need for a comprehensive investigation of the latest methods in multivariate statistics.

Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em­ pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the soeial seiences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly ab out linear least squares applied to fit a single equation (as opposed to a system of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-Iong course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, pharmacology, engineering, economics, forestry and the behav­ ioral seiences. Parts of the book have also been used in a quarter-Iong course given to Master's and Ph.D. students in public administration, urban plan­ ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and critieisms from students helped forge the final version.

Get up-to-speed on the latest methods of multivariate statisticsMultivariate statistical methods provide a powerful tool for analyzing data when observations are taken over a period of time on the same subject. With the advent of fast and efficient computers and the availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within reach of most researchers and data analysts. With an emphasis on computing techniques in combination with a full understanding of the mathematics behind the methods, Methods of Multivariate Statistics offers an up-to-date account of multivariate methods. Focusing on the maximum likelihood method for estimation, testing of hypotheses, and "profile analysis," this book offers comprehensive discussions of commonly encountered multivariate data and also covers some practical and important problems lacking in other texts. These include:* Missing at-random observations* "Growth Curve Models" and multivariate one-sided tests applicable in pharmaceutical and medical trials* Bootstrap methods* Principal component method for predicting a multivariate response vector* Outlier detection and handling inference when covariance is singularWith clear chapter introductions and numerous problem sets, Methods of Multivariate Statistics meets every statistician's need for a comprehensive investigation of the latest methods in multivariate statistics.

Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em­ pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the soeial seiences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly ab out linear least squares applied to fit a single equation (as opposed to a system of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-Iong course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, pharmacology, engineering, economics, forestry and the behav­ ioral seiences. Parts of the book have also been used in a quarter-Iong course given to Master's and Ph.D. students in public administration, urban plan­ ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and critieisms from students helped forge the final version.