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010   $a3-031-21480-3
024 7 $a10.1007/978-3-031-21480-6
035   $a(MiAaPQ)EBC7255389
035   $a(Au-PeEL)EBL7255389
035   $a(OCoLC)1381713790
035   $a(DE-He213)978-3-031-21480-6
035   $a(BIP)085993184
035   $a(PPN)272260614
035   $a(CKB)26830196500041
035   $a(EXLCZ)9926830196500041
100   $a20230602d2023      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplied Linear Regression for Business Analytics with R $eA Practical Guide to Data Science with Case Studies /$fby Daniel P. McGibney
205   $a1st ed. 2023.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023.
215   $a1 online resource (286 pages)
225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v337
311 08$aPrint version: McGibney, Daniel P. Applied Linear Regression for Business Analytics with R Cham : Springer International Publishing AG,c2023 9783031214790 
320   $aIncludes bibliographical references.
327   $a1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.
330   $aApplied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
410  0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v337
606   $aOperations research
606   $aRegression analysis
606   $aBusiness information services
606   $aBusiness$xData processing
606   $aMathematical statistics$xData processing
606   $aOperations Research and Decision Theory
606   $aLinear Models and Regression
606   $aIT in Business
606   $aBusiness Analytics
606   $aStatistics and Computing
615  0$aOperations research.
615  0$aRegression analysis.
615  0$aBusiness information services.
615  0$aBusiness$xData processing.
615  0$aMathematical statistics$xData processing.
615 14$aOperations Research and Decision Theory.
615 24$aLinear Models and Regression.
615 24$aIT in Business.
615 24$aBusiness Analytics.
615 24$aStatistics and Computing.
676   $a650.0285
676   $a650.0285
700   $aMcGibney$b Daniel P.$01365685
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910728934203321
996   $aApplied Linear Regression for Business Analytics with R$93387840
997   $aUNINA