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020 _a9783031214806
024 7 _a10.1007/978-3-031-21480-6
_2doi
040 _aES-VaUEC
_bspa
_cES-VaUEC
_dES-VaUE
050 4 _aT57.6
_b2023 EB
100 1 0 _aMcGibney, Daniel P.
_eautor
_4http://id.loc.gov/vocabulary/relators/aut
_9518792
245 1 0 _aApplied Linear Regression for Business Analytics with R :
_bA Practical Guide to Data Science with Case Studies
_cby Daniel P McGibney
250 _a1st ed 2023
264 1 _aCham
_c2023
_bSpringer International Publishing
300 0 0 _a1 recurso en línea
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
490 0 _aInternational Series in Operations Research & Management Science
_x2214-7934 ;
_v337
505 0 _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.
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.
988 _aSpringer_Business_2023
650 7 _2embne
_96736
_aAnálisis de sistemas
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-21480-6
_zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942 _2lcc
_cLE
998 _b06/2024
_db
_ej
_zSI