Applied linear regression for business analytics with R : a practical guide to data science with case studies / Daniel P. McGibney.
- 作者： McGibney, Daniel P.
- 其他題名：
- International series in operations research & management science ;
- 出版： Cham, Switzerland : Springer c2023.
- 叢書名： International series in operations research & management science,v. 337
- 主題： Business--Data processing. , R (Computer program language) , Regression analysis.
- ISBN： 9783031214820 (pbk.): NT$2872 、 9783031214790 (hbk.)
- 書目註：Includes bibliographical references (p. 275-276).
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讀者標籤：
- 系統號： 005182462 | 機讀編目格式
館藏資訊
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.
摘要註
"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"--
內容註
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.