The goal of this book is to provide the readers with examples of application of a wide set of quantitative methods for Quality Management, making it a useful resource for both students and practitioners who are willing to understand the basics of statistical thinking applied to industrial domains (both Manufacturing and Service operations). Students will find the ideal support and guidance for getting confident with the subject, while practitioners will be provided with theoretical and practical insights to deeply understand the ground on which most of commonly used tools are built on. The structure of the book is the logical structure of an exercise book applied to a scientific topic such as statistics and quantitative methods: starting from the easiest-to-understand subject (descriptive statistic), the reader will experience an increasing level of complexity in the tools adopted and in the numerical examples, as the knowledge of the topic should be growing through the chapters. Every chapters opens with a short review of the basic theories to approach the exercises then presented: these theoretical introductions are not to be regarded as a substitute of statistical text-books; rather, as a handy vademecum of the most important algorithms and formulae to be known to correctly approach the problem solving. Some of the exercises presented in the various chapters are not solved: pedagogically, we decided to illustrate the methods and problem-solving approach in the solved exercises, while leaving to the reader the task of applying the same approach and method to the ones without solution. This second, revised and expanded edition of the book comes with an updated and larger set of exercises, endeavoring to turn an academic textbook into a pragmatical manual, useful also in real business contexts. We would like to thank all the students and readers who gave us feed-backs on the first edition (2008) in order to improve it before printing the new edition (2012). Should you have any comment or feedback, it would be greatly appreciated.
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