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  140301s2014 xx o 000 0 eng d

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  ▼a  9781317780236
  ▼q  (electronic bk.)

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  ▼a  131778023X
  ▼q  (electronic bk.)

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  ▼a  AU@
  ▼b  000052900839

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  ▼a  DEBBG
  ▼b  BV043607841

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  ▼a  DEBSZ
  ▼b  405663595

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  ▼a  (OCoLC)871224592

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  ▼a  EBLCP
  ▼b  eng
  ▼e  pn
  ▼c  EBLCP
  ▼d  MHW
  ▼d  OCLCQ
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  ▼d  OCLCQ
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  ▼d  OCLCF
  ▼d  OCLCQ
  ▼d  248023

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  ▼a  QA278 .W53 2014

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  ▼a  MAT
  ▼x  003000
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  ▼a  MAT
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  ▼a  519.535

• 100

  ▼a  Wickens, Thomas D.

• 245

  ▼a  The Geometry of Multivariate Statistics.

• 260

  ▼a  Hoboken:
  ▼b  Taylor and Francis,
  ▼c  2014.

• 300

  ▼a  1 online resource (174 pages).

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  ▼a  text
  ▼b  txt
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  ▼a  computer
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  ▼a  online resource
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  ▼a  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.

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  ▼a  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.

• 505

  ▼a  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.

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  ▼a  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.

• 588

  ▼a  Print version record.

• 590

  ▼a  eBooks on EBSCOhost
  ▼b  All EBSCO eBooks

• 650

  ▼a  Multivariate analysis.

• 650

  ▼a  Vector analysis.

• 650

  ▼a  MATHEMATICS
  ▼x  Applied.
  ▼2  bisacsh

• 650

  ▼a  MATHEMATICS
  ▼x  Probability & Statistics
  ▼x  General.
  ▼2  bisacsh

• 650

  ▼a  Multivariate analysis.
  ▼2  fast
  ▼0  (OCoLC)fst01029105

• 650

  ▼a  Vector analysis.
  ▼2  fast
  ▼0  (OCoLC)fst01164651

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Wickens, Thomas D.
  ▼t  Geometry of Multivariate Statistics.
  ▼d  Hoboken : Taylor and Francis, ©2014,
  ▼z  9780805816563

• 856

  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=881558

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  ▼a  EBL - Ebook Library
  ▼b  EBLB
  ▼n  EBL1639241

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  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  881558

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  ▼a  강리원

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  ▼a  eBook

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  ▼a  92
  ▼b  KRKUC