• Book Name: Business Analytics Data Analysis and Decision Making
• Author: S. Christian Albright, Wayne L. Winston
• Pages: 1180
• Size: 23 MB

Business Analytics Data Analysis and Decision Making

With today’s technology, companies are able to collect tremendous amounts of data with relative ease. Indeed, many companies now have more data than they can handle. However, the data are usually meaningless until they are analyzed for trends, patterns, relationships, and other useful information. This book illustrates in a practical way a variety of methods, from simple to complex, to help you analyze data sets and uncover important information. In many business contexts, data analysis is only the irst step in the solution of a problem. Acting on the solution and the information it provides to make good decisions is a critical next step.

Therefore, there is a heavy emphasis throughout this book on analytical methods that are useful in decision making. Again, the methods vary considerably, but the objective is always the same—to equip you with decision-making tools that you can apply in your business careers. We recognize that the majority of students in this type of course are not majoring in a quantitative area. They are typically business majors in inance, marketing, operations management, or some other business discipline who will need to analyze data and make quantitative-based decisions in their jobs. We offer a hands-on, example-based approach and introduce fundamental concepts as they are needed. Our vehicle is spreadsheet software—speciically, Microsoft Excel® .

This is a package that most students already know and will almost surely use in their careers. Our MBA students at Indiana University are so turned on by the required course that is based on this book that almost all of them (mostly inance and marketing majors) take at least one of our follow-up elective courses in spreadsheet modeling. We are convinced that students see value in quantitative analysis when the course is taught in a practical and example-based approach. Rationale for Writing This Book Business Analytics: Data Analysis and Decision Making is different from the many ine textbooks written for statistics and management science. Our rationale for writing this book is based on three fundamental objectives.

Business Analytics Data Analysis and Decision Making

■ Integrated coverage and applications. The book provides a uniied approach to business-related problems by integrating methods and applications that have been traditionally taught in separate courses, speciically statistics and management science.

■ Practical in approach. The book emphasizes realistic business examples and the processes managers actually use to analyze business problems. The emphasis is not on abstract theory or computational methods.

■ Spreadsheet-based teaching. The book provides students with the skills to analyze business problems with tools they have access to and will use in their careers. To this end, we have adopted Excel and commercial spreadsheet add-ins.

Integrated Coverage and Applications

In the past, many business schools, including ours at Indiana University, have offered a required statistics course, a required decision-making course, and a required management science course—or some subset of these. A current trend, however, is to have only one required course that covers the basics of statistics, some regression analysis, some decision making under uncertainty, some linear programming, some simulation, and possibly others. Essentially, faculty in the quantitative area get one opportunity to teach all business students, so we attempt to cover a variety of useful quantitative methods. We are not necessarily arguing that this trend is ideal, but rather that it is a reection of the reality at our university and, we suspect, at many others. After several years of teaching this course, we have found it to be a great opportunity to attract students to the subject and to more advanced study. The book is also integrative in another important aspect. It not only integrates a number of analytical methods, but it also applies them to a wide variety of business problems—that is, it analyzes realistic examples from many business disciplines. We include examples, problems, and cases that deal with portfolio optimization, workforce scheduling, market share analysis, capital budgeting, new product analysis, and many others.

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