Abstract
Business analytics uses modern computing methods to report, enhance, and provide insights into modern businesses. Regression analysis does these actions through data by predicting unknown values, assessing differences among groups, and checking the relationships among variables. When regression analysis is applied to the right data set in the right way, the results can make businesses extremely profitable, whether the objective is predicting the sale price of houses, assessing marketing methods, or predicting the number of likes on a social media post. This book has countless applications of business examples where regression analysis produces valuable insights. This chapter begins with a discussion of the history of regression analysis and its role in data science, machine learning, and artificial intelligence (AI). Also, we will provide an overview of each of the eight case studies in this book. These cases offer detailed analyses of how to use regression analysis to obtain actionable business findings.
In God we trust. All others must bring data.
—W. Edwards Deming
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McGibney, D.P. (2023). Introduction. In: Applied Linear Regression for Business Analytics with R. International Series in Operations Research & Management Science, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-031-21480-6_1
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DOI: https://doi.org/10.1007/978-3-031-21480-6_1
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