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