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Aspects of Multivariate Statistical Analysis in Geology [Hardcover]

E. Savazzi (Author), R.A. Reyment (Author)


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Book Description

December 8, 1999 0444825681 978-0444825681
The book presents multivariate statistical methods useful in geological analysis. The essential distinction between multivariate analysis as applied to full-space data (measurements on lengths, heights, breadths etc.) and compositional data is emphasized with particular reference to geochemical data. Each of the methods is accompanied by a practically oriented computer program and backed up by appropriate examples. The computer programs are provided on a compact disk together with trial data-sets and examples of the output.

An important feature of this book is the graphical system developed by Dr. Savazzi which is entitled Graph Server. Geological data often deviate from ideal statistical requirements. For this reason, close attention has been paid to the analysis of data that contain atypical observations.


Editorial Reviews

Review

(A.F. Militino, Universidad Pùblica de Navarra, Spain)
This book contributes to a better understanding of multivariate statistical analysis in the earth sciences.(...)this monograph is a good support and a textbook addressed to students, researchers, instructors and general practitioners of multivariate statistical techniques in the geosciences and it is highly recommended.
Statistical Software Newsletter, Vol. 34, No. 4

Product Details

  • Hardcover: 292 pages
  • Publisher: Elsevier Science (December 8, 1999)
  • Language: English
  • ISBN-10: 0444825681
  • ISBN-13: 978-0444825681
  • Product Dimensions: 9.5 x 6.8 x 0.8 inches
  • Shipping Weight: 1.6 pounds
  • Amazon Best Sellers Rank: #9,112,322 in Books (See Top 100 in Books)

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Inside This Book (learn more)
First Sentence:
We have endeavoured to make our text "transparent", yet self-contained. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
generalized statistical distance, current drawing position, common principal component model, common principal component analysis, latent vectors, observational vectors, corresponding latent roots, jackknifed estimates, canonical root, canonical variate analysis, shrinkage constants, shrunken estimators, simplex space, graphical engine, principal component coefficients, quadratic discriminant function, principal coordinate analysis, principal component results, atypical observations, compositional data, common principal components, current pen, test stat, principal component loadings, canonical vectors
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Graph Server, Graph Wizard, Earth Sciences, Karl Pearson, Font Inclination, Lower Cycle, North Sea, Fill Color, Graph Serrrr, Pacific Ocean, Specimen Coordinates, Trial Number, Variable Mean Standard
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