MARC record from Internet Archive

LEADER: 02326cam a2200349 a 4500
001 2007275525
003 DLC
005 20070626085622.0
008 070524s2006 ncua 001 0 eng d
010 $a 2007275525
020 $a9781599940472
020 $a1599940477
035 $a(OCoLC)ocm82171634
040 $aSA$$cSA$$dSA$$dBAKER$dDLC
042 $alccopycat
050 00 $aHF5548.2$b.S875 2006
100 1 $aSvolba, Gerhard.
245 10 $aData preparation for analytics using SAS /$cGerhard Svolba.
260 $aCary, NC :$bSAS Institute,$c2006.
300 $axxii, 408 p. :$bill. ;$c28 cm.
440 0 $aSAS Press series
500 $aIncludes index.
505 0 $aPt. 1. Data preparation: business point of view -- ch. 1. Analytic business questions -- Ch. 2. Characteristics of analytic business questions -- Ch. 3. Characteristics of data sources -- Ch. 4. Different points of view on analytic data preparation -- Pt. 2. Data structures and data modeling -- Ch. 5. The origin of data -- Ch. 6. Data models -- Ch. 7. Analysis subjects and multiple observations -- Ch. 8. The one row-per-subject data mart -- Ch. 9. The multiple-rows-per-subject data mart -- Ch. 10. Data structures for longitudinal analysis -- Ch. 11. Considerations for data marts -- Ch. 11. Considerations for predictive modeling -- Pt. 3. Data mart coding and content -- Ch. 13. Accessing data -- Ch. 14. Transposing one- and multiple-rows-per-subject data structures -- Ch. 15. Transposing longitudinal data -- Ch. 16. Transformations of interval-scaled variables -- Ch. 17. Transformations of categorical variables -- Ch. 18. Multiple interval-scaled observations per subject -- Ch. 19. Multiple catagorical observations per subject -- Ch. 20. Coding for predictive modeling -- Ch. 21. Data preparation for multiple-rows-per-subject and longitudinal data marts -- Pt. 4. Sampling, scoring, and automation -- Ch. 22. Sampling -- Ch. 23. Scoring and automation -- Ch 24. Do's and don'ts when building data marts -- Pt. 5. Case studies.
630 00 $aSAS (Computer file)
630 00 $aEnterprise Miner.
650 0 $aBusiness$xData processing.
650 0 $aElectronic data processing.
650 0 $aCommercial analysis.
650 0 $aData marts.
650 0 $aData mining.
650 0 $aTime-series analysis.
710 2 $aSAS Institute.