Applied Linear Regression for Business Analytics with R: A Practical Guide to Data Science with Case Studies

Applied Linear Regression for Business Analytics with R is a book that uses R programming to teach regression analysis to business students, focusing on real-world case studies and practical analysis.

Format: Hardback
Length: 276 pages
Publication date: 03 June 2023
Publisher: Springer International Publishing AG

Applied Linear Regression for Business Analytics with R is a comprehensive guide that introduces regression analysis to business students using the R programming language. With a strong emphasis on practical application, the book provides a wealth of real-world case studies and examples to illustrate the theory and principles of regression modeling. It offers clear and concise explanations of the underlying concepts, along with hands-on exercises and coding exercises to help students develop their skills in applying regression techniques to solve business problems.

The book begins by covering the basic concepts of linear regression, including the definition of the regression model, the assumptions underlying the analysis, and the methods used to estimate the model parameters. It then moves on to discuss the importance of data preparation and selection, exploring techniques such as data cleaning, outlier detection, and variable transformation.

Throughout the book, modern and relevant case studies from various industries are presented, showcasing the practical applications of regression analysis. Each case study includes a problem statement, data analysis, and the results of the regression analysis, allowing students to apply their knowledge to real-world scenarios.

In addition to the theoretical explanations, the book provides detailed mathematical formulations and R programming code for each chapter. This allows students to understand the underlying algorithms and apply them to their own data analysis projects.

Furthermore, the book includes a comprehensive appendix that covers important topics such as hypothesis testing, model validation, and interpretation of regression results. This appendix provides students with the tools they need to evaluate the effectiveness of their regression models and make informed decisions based on the data.

Overall, Applied Linear Regression for Business Analytics with R is an essential resource for business students who want to gain a deeper understanding of regression analysis and its applications in business. With its comprehensive coverage, practical examples, and detailed coding exercises, the book provides students with the skills and knowledge they need to succeed in their studies and careers in business analytics.

Applied Linear Regression for Business Analytics with R is a comprehensive guide that introduces regression analysis to business students using the R programming language. With a strong emphasis on practical application, the book provides a wealth of real-world case studies and examples to illustrate the theory and principles of regression modeling. It offers clear and concise explanations of the underlying concepts, along with hands-on exercises and coding exercises to help students develop their skills in applying regression techniques to solve business problems.

The book begins by covering the basic concepts of linear regression, including the definition of the regression model, the assumptions underlying the analysis, and the methods used to estimate the model parameters. It then moves on to discuss the importance of data preparation and selection, exploring techniques such as data cleaning, outlier detection, and variable transformation.

Throughout the book, modern and relevant case studies from various industries are presented, showcasing the practical applications of regression analysis. Each case study includes a problem statement, data analysis, and the results of the regression analysis, allowing students to apply their knowledge to real-world scenarios.

In addition to the theoretical explanations, the book provides detailed mathematical formulations and R programming code for each chapter. This allows students to understand the underlying algorithms and apply them to their own data analysis projects.

Furthermore, the book includes a comprehensive appendix that covers important topics such as hypothesis testing, model validation, and interpretation of regression results. This appendix provides students with the tools they need to evaluate the effectiveness of their regression models and make informed decisions based on the data.

Overall, Applied Linear Regression for Business Analytics with R is an essential resource for business students who want to gain a deeper understanding of regression analysis and its applications in business. With its comprehensive coverage, practical examples, and detailed coding exercises, the book provides students with the skills and knowledge they need to succeed in their studies and careers in business analytics.

Weight: 566g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031214790
Edition number: 1st ed. 2023