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

by Daniel P. McGibney

Overview

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.

Product Details

ISBN-13:	9783031214790
Publisher:	Springer International Publishing
Publication date:	06/03/2023
Series:	International Series in Operations Research & Management Science , #337
Edition description:	2023
Pages:	276
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Dr. Daniel McGibney is an Assistant Professor of Professional Practice at the University of Miami Herbert Business School, USA. He currently teaches analytics to both graduate and undergraduate students. Over the years, he has taught many analytics and data science classes, ranging from Basic Statistics to Big Data Analytics and Deep Learning. He has taught Applied Linear Regression Analysis to students pursuing their MSBA, MBA, MST, and MAcc. He also actively oversees and mentors graduate capstone projects in Analytics for MSBA students, collaborating with Deloitte, Visa, Carnival, Citi, Experian, and many other companies. Dr. McGibney formerly served as the program director for the Herbert Business School’s MSBA degree program. He advised students, oversaw admissions, expanded industry partnerships, and advanced the program curriculum during his tenure as program director.

Table of Contents

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.