Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies
McGibney, Daniel P.
Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies by Daniel P McGibney - 1st ed 2023 - 1 recurso en línea - International Series in Operations Research & Management Science 337 2214-7934 ; .
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.
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.
9783031214806
10.1007/978-3-031-21480-6 doi
Análisis de sistemas
T57.6 / 2023 EB
Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies by Daniel P McGibney - 1st ed 2023 - 1 recurso en línea - International Series in Operations Research & Management Science 337 2214-7934 ; .
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.
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.
9783031214806
10.1007/978-3-031-21480-6 doi
Análisis de sistemas
T57.6 / 2023 EB