Applied_Multivariate_Statistics_with_SAS_Software__Second_Edition_-_fly_2 rar

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Approach of the Book:
Primary emphasis is on statistical methodology as applied to various scientic disciplines.
SAS software is used as the crucial computational aid to carry out various intensive calcu-
lations which so naturally occur in any typical multivariate analysis application. Discussion
in this volume is limited to only the normal theory-based multivariate analysis.
We believe that those who use multivariate methods should not only understand appro-
priate statistical techniques useful in their particular situation but should also be able to
discern the appropriate approach and distinguish it from an approach that seems correct
but is completely inappropriate in a particular context. Quite often, these differences are
subtle, and there are scenarios where the presumably best approach may be completely
invalid due to one reason or the other. The problem is further compounded by the under-
standable temptation to take the shortest route by choosing the analysis that can be readily
performed using a particular software package or a canned computer program, regardless
of its appropriateness, over a more appropriate analysis not so readily available. This book
attempts to demonstrate this process of discernment, problem denition, selection of an
appropriate analysis or a combination of many, while providing both the needed SAS code
to achieve these goals and the subsequent interpretation of the SAS output.
This approach largely eliminates the need for two books, one for learning multivariate
techniques and another for mastering the software usage. Instead of taking various multi-
variate procedures in SAS one at a time and demonstrating their potential to solve a large
number of different problems, we have chosen to discuss various multivariate situations
one by one and then identify the most appropriate SAS analyses for them. Many of these
analyses may occasionally result from the combined applications of two or more SAS pro-
cedures. All multivariate methods are illustrated by appropriate examples. In most cases,
the data sets considered are real and are adapted from the published literature from a variety
of disciplines.
Authors: Ravindra Khattree and Dayanand N. Naik
Edition: 2nd
Pages: 363
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