Quantitative Methods for Management A Practical Approach /

• Κύριοι συγγραφείς: Canela, Miguel Ángel (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Alegre, Inés (http://id.loc.gov/vocabulary/relators/aut), Ibarra, Alberto (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Production management.; Engineering economics.; Engineering economy.; Operations research.; Management science.; Statistics .; Operations Management.; Engineering Economics, Organization, Logistics, Marketing.; Operations Research, Management Science.; Statistics for Business, Management, Economics, Finance, Insurance.

 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. . 

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