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[null,"1.","Record Nr.",null,"UNINA9910728934203321"]
[null,null,"Autore",null,"McGibney Daniel P."]
[null,null,"Titolo",null,"Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies / / by Daniel P. McGibney"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"3-031-21480-3"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (286 pages)"]
[null,null,"Collana",null,"International Series in Operations Research & Management Science, , 2214-7934 ; ; 337"]
[null,null,"Disciplina",null,"650.0285"]
[null,null,"Soggetti",null,"Operations research"]
[{"$nulls":4},"Regression analysis"]
[{"$nulls":4},"Business information services"]
[{"$nulls":4},"Business - Data processing"]
[{"$nulls":4},"Mathematical statistics - Data processing"]
[{"$nulls":4},"Operations Research and Decision Theory"]
[{"$nulls":4},"Linear Models and Regression"]
[{"$nulls":4},"IT in Business"]
[{"$nulls":4},"Business Analytics"]
[{"$nulls":4},"Statistics and Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"1. 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."]
[null,null,null,"Sommario/riassunto","Applied 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."]