| Record ID | marc_columbia/Columbia-extract-20221130-009.mrc:290093354:6995 |
| Source | marc_columbia |
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LEADER: 06995cam a2201009Ma 4500
001 4250516
005 20221111173519.0
006 m o d
007 cr cn|||||||||
008 001213s1998 nyua ob 001 0 eng d
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060 4 $aQA 278.2 D7 1981
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100 1 $aDraper, Norman Richard.
245 10 $aApplied regression analysis /$cNorman R. Draper, Harry Smith.
250 $a3rd ed.
260 $aNew York :$bWiley,$c©1998.
300 $a1 online resource (xvii, 706 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aWiley series in probability and statistics. Texts and references section
504 $aIncludes bibliographical references and indexes.
505 0 $a0. Basic Prerequisite Knowledge -- 1. Fitting a Straight Line by Least Squares -- 2. Checking the Straight Line Fit -- 3. Fitting Straight Lines: Special Topics -- 4. Regression in Matrix Terms: Straight Line Case -- 5. The General Regression Situation -- 6. Extra Sums of Squares and Tests for Several Parameters Being Zero -- 7. Serial Correlation in the Residuals and the Durbin-Watson Test -- 8. More on Checking Fitted Models -- 9. Multiple Regression: Special Topics -- 10. Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares -- 11. On Worthwhile Regressions, Big F's, and R[superscript 2] -- 12. Models Containing Functions of the Predictors, Including Polynomial Models -- 13. Transformation of the Response Variable -- 14. "Dummy" Variables -- 15. Selecting the "Best" Regression Equation -- 16. Ill-Conditioning in Regression Data -- 17. Ridge Regression -- 18. Generalized Linear Models (GLIM) -- 19. Mixture Ingredients as Predictor Variables -- 20. The Geometry of Least Squares -- 21. More Geometry of Least Squares -- 22. Orthogonal Polynomials and Summary Data -- 23. Multiple Regression Applied to Analysis of Variance Problems -- 24. An Introduction to Nonlinear Estimation -- 25. Robust Regression -- 26. Resampling Procedures (Bootstrapping).
588 0 $aPrint version record.
506 $3Use copy$fRestrictions unspecified$2star$5MiAaHDL
533 $aElectronic reproduction.$b[Place of publication not identified] :$cHathiTrust Digital Library,$d2011.$5MiAaHDL
538 $aMaster and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.$uhttp://purl.oclc.org/DLF/benchrepro0212$5MiAaHDL
583 1 $adigitized$c2011$hHathiTrust Digital Library$lcommitted to preserve$2pda$5MiAaHDL
520 $aAn outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, u.
650 0 $aRegression analysis.
650 2 $aRegression Analysis
650 6 $aAnalyse de régression.
650 7 $aMATHEMATICS$xApplied.$2bisacsh
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aMéthodes statistiques.$2eclas
650 7 $aAnalyse statistique.$2eclas
650 7 $aRegression analysis.$2fast$0(OCoLC)fst01432090
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650 7 $aREGRESSÃO (ANÁLISE)$2larpcal
650 7 $aAnalyse de régression.$2ram
650 7 $aProbability & statistics.$2thema
650 7 $aMathematics.$2ukslc
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aSmith, Harry,$d1923-2012.
776 08 $iPrint version:$aDraper, Norman Richard.$tApplied regression analysis.$b3rd ed.$dNew York : Wiley, ©1998$z0471170828$w(DLC) 97017969$w(OCoLC)37141097
830 0 $aWiley series in probability and statistics.$pTexts and references section.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio4250516$zAll EBSCO eBooks
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