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The Geometry of Multivariate Statistics (Hardcover)
Loot Price: R4,698
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The Geometry of Multivariate Statistics (Hardcover)
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A traditional approach to developing multivariate statistical
theory is algebraic. Sets of observations are represented by
matrices, linear combinations are formed from these matrices by
multiplying them by coefficient matrices, and useful statistics are
found by imposing various criteria of optimization on these
combinations. Matrix algebra is the vehicle for these calculations.
A second approach is computational. Since many users find that they
do not need to know the mathematical basis of the techniques as
long as they have a way to transform data into results, the
computation can be done by a package of computer programs that
somebody else has written. An approach from this perspective
emphasizes how the computer packages are used, and is usually
coupled with rules that allow one to extract the most important
numbers from the output and interpret them. Useful as both
approaches are--particularly when combined--they can overlook an
important aspect of multivariate analysis. To apply it correctly,
one needs a way to conceptualize the multivariate relationships
that exist among variables.
This book is designed to help the reader develop a way of thinking
about multivariate statistics, as well as to understand in a
broader and more intuitive sense what the procedures do and how
their results are interpreted. Presenting important procedures of
multivariate statistical theory geometrically, the author hopes
that this emphasis on the geometry will give the reader a coherent
picture into which all the multivariate techniques fit.
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