자료유형  

 단행본

Control Number  

n871224592

International Standard Book Number  

9781317780236 (electronic bk.)

International Standard Book Number  

131778023X (electronic bk.)

Library of Congress Call Number  

QA278 .W53 2014

Dewey Decimal Classification Number  

519.535

Main Entry-Personal Name  

Wickens, Thomas D.

Publication, Distribution, etc. (Imprint  

Hoboken : Taylor and Francis, 2014

Physical Description  

1 online resource (174 pages)

Formatted Contents Note  

완전내용Cover; Title Page; Copyright Page; Table of Contents; 1 Variable space and subject space; 2 Some vector geometry; 2.1 Elementary operations on vectors; 2.2 Variables and vectors; 2.3 Vector spaces; 2.4 Linear dependence and independence; 2.5 Projection onto subspaces; 3 Bivariate regression; 3.1 Selecting the regression vector; 3.2 Measuring goodness of fit; 3.3 Means and the regression intercept; 3.4 The difference between two means; 4 Multiple regression; 4.1 The geometry of prediction; 4.2 Measuring goodness of fit; 4.3 Interpreting a regression vector.

Formatted Contents Note  

완전내용5 Configurations of regression vectors5.1 Linearly dependent predictors; 5.2 Nearly multicollinear predictors; 5.3 Orthogonal predictors; 5.4 Suppressor variables; 6 Statistical tests; 6.1 The effect space and the error space; 6.2 The population regression model; 6.3 Testing the regression effects; 6.4 Parameter restrictions; 7 Conditional relationships; 7.1 Partial correlation; 7.2 Conditional effects in multiple regression; 7.3 Statistical tests of conditional effects; 8 The analysis of variance; 8.1 Representing group differences; 8.2 Unequal sample sizes; 8.3 Factorial designs.

Formatted Contents Note  

완전내용8.4 The analysis of covariance9 Principal-component analysis; 9.1 Principal-component vectors; 9.2 Variable-space representation; 9.3 Simplifying the variables; 9.4 Factor analysis; 10 Canonical correlation; 10.1 Angular relationships between spaces; 10.2 The sequence of canonical triplets; 10.3 Test statistics; 10.4 The multivariate analysis of variance; Index.

Summary, Etc.  

요약A 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 b.

Subject Added Entry-Topical Term  

Multivariate analysis

Subject Added Entry-Topical Term  

Vector analysis

Subject Added Entry-Topical Term  

MATHEMATICS Applied.

Subject Added Entry-Topical Term  

MATHEMATICS Probability & Statistics General.

Subject Added Entry-Topical Term  

Multivariate analysis.

Subject Added Entry-Topical Term  

Vector analysis.

Additional Physical Form Entry  

Print versionWickens, Thomas D. Geometry of Multivariate Statistics. Hoboken : Taylor and Francis, ©2014 9780805816563

Electronic Location and Access  

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Control Number  

joongbu:498849