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001469461 001__ 1469461
001469461 003__ OCoLC
001469461 005__ 20230803003331.0
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001469461 008__ 230607s2023\\\\sz\\\\\\ob\\\\000\0\eng\d
001469461 019__ $$a1381713790$$a1382626555$$a1388521334$$a1390568099
001469461 020__ $$a9783031214806$$q(electronic bk.)
001469461 020__ $$a3031214803$$q(electronic bk.)
001469461 020__ $$z303121479X
001469461 020__ $$z9783031214790
001469461 0247_ $$a10.1007/978-3-031-21480-6$$2doi
001469461 035__ $$aSP(OCoLC)1381202437
001469461 040__ $$aYDX$$beng$$erda$$cYDX$$dEBLCP$$dGW5XE$$dUKAHL$$dOCLCF$$dYDX
001469461 049__ $$aISEA
001469461 050_4 $$aHD30.2$$b.M34 2023
001469461 08204 $$a650.0285$$223
001469461 1001_ $$aMcGibney, Daniel P.,$$eauthor.
001469461 24510 $$aApplied linear regression for business analytics with R :$$ba practical guide to data science with case studies /$$cDaniel P. McGibney.
001469461 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001469461 300__ $$a1 online resource.
001469461 336__ $$atext$$btxt$$2rdacontent
001469461 337__ $$acomputer$$bc$$2rdamedia
001469461 338__ $$aonline resource$$bcr$$2rdacarrier
001469461 4901_ $$aInternational Series in Operations Research & Management Science ;$$vvolume 337
001469461 504__ $$aIncludes bibliographical references.
001469461 5050_ $$a1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.
001469461 506__ $$aAccess limited to authorized users.
001469461 520__ $$aApplied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
001469461 588__ $$aDescription based on online resource; title from digital title page (viewed on July 18, 2023).
001469461 650_0 $$aBusiness$$xData processing.
001469461 650_0 $$aR (Computer program language)
001469461 650_0 $$aRegression analysis.
001469461 655_0 $$aElectronic books.
001469461 77608 $$iPrint version: $$z303121479X$$z9783031214790$$w(OCoLC)1347786097
001469461 830_0 $$aInternational series in operations research & management science ;$$v337.
001469461 852__ $$bebk
001469461 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-21480-6$$zOnline Access$$91397441.1
001469461 909CO $$ooai:library.usi.edu:1469461$$pGLOBAL_SET
001469461 980__ $$aBIB
001469461 980__ $$aEBOOK
001469461 982__ $$aEbook
001469461 983__ $$aOnline
001469461 994__ $$a92$$bISE