Principles of Business Analytics

This textbook is intended to support the teaching of introductory business analytics at the under-graduate and graduate levels. The book is divided into two parts—Mathematical Methods and Sta-tistical Methods. In Mathematical Methods, we introduce decision analysis with the study of influence diagrams and decision trees—two different tools to aid with the construction, visualization, and analysis of models for decision problems. Linear programming covers a broad category of optimization problems with myriad applications in business settings. Throughout, we focus on translating a business problem into a mathematical model, implementing the model using software, and analyzing the solution to the problem with the tools of sensitivity analysis. In Statistical Methods, we analyze the relationship between variables by building models to repre-sent, understand, and predict the behavior of the phenomena in question. The regression model is studied in detail—from linear regression to logistic regression—with emphasis on the correct appli-cation to a particular dataset. The tools presented in this section serve as a starting point for further data analysis, an essential skill for emerging managers.