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The Geometry of Multivariate Statistics
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Wickens, Thomas D. |
| 서명/저자사항 | The Geometry of Multivariate Statistics. |
| 발행사항 | Hoboken : Taylor and Francis, 2014. |
| 형태사항 | 1 online resource (174 pages) |
| 소장본 주기 | eBooks on EBSCOhostAll EBSCO eBooks |
| ISBN | 9781317780236 131778023X |
| 내용주기 | Cover; 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. 5 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. 8.4 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. |
| 요약 | 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 b. |
| 일반주제명 | Multivariate analysis. Vector analysis. MATHEMATICS -- Applied. MATHEMATICS -- Probability & Statistics -- General. Multivariate analysis. Vector analysis. |
| 언어 | 영어 |
| 기타형태 저록 | Print version:Wickens, Thomas D.Geometry of Multivariate Statistics.Hoboken : Taylor and Francis, 짤20149780805816563 |
| 대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=881558 |
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|---|---|---|---|---|---|---|---|---|
| 1 | WE00007559 | 519.535 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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