Geometry of Multivariate Statistics

Regular price €72.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Thomas D. Wickens
Algebraic Vector
Author_Thomas D. Wickens
Canonical Components
canonical correlation
Canonical Correlation Analysis
Canonical Variables
Canonical Vectors
Category=GPS
Category=JB
Category=JHB
Category=JMB
Category=PBT
complement
conditional dependence
Confirmatory Factor Structure
Discriminant Vectors
dot
dummy
Dummy Variable
Dummy Vectors
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
geometric approach to multivariate statistics
Geometric Vector
gestalt
GESTALT GESTALT
GESTALT GESTALT GESTALT
GESTALT GESTALT GESTALT GESTALT
Higher Order Vectors
Low Dimensional Solution
multiple
Multiple Correlation Coefficient
Original Principal Components
orthogonal
Orthogonal Complement
Polynomial Trend Analysis
Principal Component Analysis
Principal Component Vectors
regression
regression analysis
Regression Space
Regression Vector
space
Space Vx
statistical modeling
subject
variance analysis
vector spaces
vectors

Product details

  • ISBN 9781138882829
  • Weight: 300g
  • Dimensions: 152 x 229mm
  • Publication Date: 07 May 2015
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns

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.

University of California, Los Angeles.

More from this author