This book focuses on the use of quantitative methods for both business and management, helping readers understand the most relevant quantitative methods for managerial decision-making. Pursuing a highly practical approach, the book reduces the theoretical information to a minimum, so as to give full prominence to the analysis of real business problems. Each chapter includes a brief theoretical explanation, followed by a real-life managerial case that needs to be solved, which is accompanied by a corresponding Microsoft Excel® dataset. The practical cases and exercises are solved using Excel, and for each problem, the authors provide an Excel file with the complete solution and corresponding calculations, which can be downloaded easily from the book's website. Further, in an appendix, readers can find solutions to the same problems, but using the R statistical language. The book represents a valuable reference guide for postgraduate, MBA and executive education students, as it offers a hands-on, practical approach to learning quantitative methods in a managerial context. It will also be of interest to managers looking for a practical and straightforward way to learn about quantitative methods and improve their decision-making processes.

Autorentext

Miguel Ángel Canela is currently an Associate Professor of Managerial Decisions Sciences at IESE Business School. He holds a Ph.D. in Mathematics from the Universitat de Barcelona, Spain. Inés Alegre is currently an Assistant Professor of Managerial Decision Sciences at IESE Business School. She holds a Ph.D. in Management and a Master of Research in Management from IESE, as well as an Industrial Engineering degree from the Universitat Politècnica de Catalunya, Spain. Alberto Ibarra is currently an Assistant Professor of Decision Analysis at IPADE Business School. He holds a Ph.D. in Management from IESE Business School, an MBA from IPADE Business School and a Public Accounting degree from Universidad Autónoma de Nuevo León.

Inhalt

Part I: Basics.- Part II: Regression Analysis.- Part III: Classification.- Part IV: Times Series Data.