Kenett R. Modern Statistics...Approach with Python 2022 Fix torrent

Textbook in PDF format

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.
This book is about modern statistics with Python. It reflects many years of experience of the authors in doing research, teaching and applying statistics in science, healthcare, business, defense, and industry domains. The book invokes over 40 case studies and provides comprehensive Python applications. A special Python package, mistat, is available for download from GitHub. Everything in the book can be reproduced with mistat. We therefore provide, in this book, an integration of needs, methods, and delivery platform for a large audience and a wide range of applications.
This book can be used as textbook in a one semester or two semester course on modern statistics. The technical level of the presentation in book can serve both undergraduate and graduate students. The example and case studies provide access to hands on teaching and learning. Every chapter includes exercises, data sets, and Python applications. These can be used in regular classroom setups, flipped classroom setups, and online or hybrid education programs. The companion text is focused on industrial statistics with special chapters on advanced process monitoring methods, cybermanufacturing, computer experiments, and Bayesian reliability. Modern Statistics is a foundational text and can be combined with any program requiring data analysis in its curriculum. This, for example, can be courses in data science, industrial statistics, physics, biology, chemistry, economics, psychology, social sciences, or any engineering discipline