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010   $a1-283-20451-7
010   $a9786613204516
010   $a1-119-97646-4
010   $a1-119-97647-2
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035   $a(EBL)819260
035   $a(OCoLC)746321035
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100   $a20110505d2011      uy         0
101 0 $aeng
135   $aurcn|||||||||
181   $ctxt
182   $cc
183   $acr
200 00$aCompositional data analysis $etheory and applications /$fedited by Vera Pawlowsky-Glahn, Antonella Buccianti
205   $a1st ed.
210   $aHoboken, N.J. $cWiley$d2011
215   $a1 online resource (402 p.)
300   $aDescription based upon print version of record.
311   $a1-119-97761-4 
311   $a0-470-71135-3 
320   $aIncludes bibliographical references and index.
327   $apt. 1. Introduction -- pt. 2. Theory-- statistical modelling -- pt. 3. Theory-- algebra and calculus -- pt. 4. Applications -- pt. 5. Software.
330   $aIt is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, bi
606   $aMultivariate analysis
606   $aCorrelation (Statistics)
606   $aAnàlisi multivariable$2lemac
606   $aCorrelació (Estadística)$2lemac
615  0$aMultivariate analysis.
615  0$aCorrelation (Statistics)
615  7$aAnàlisi multivariable
615  7$aCorrelació (Estadística)
676   $a519.5/35
701   $aPawlowsky-Glahn$b Vera$0741206
701   $aBuccianti$b Antonella$0147009
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910133221303321
996   $aCompositional data analysis$92207468
997   $aUNINA