Modern Statistics: A Computer-Based Approach with Python

This textbook offers a modern approach to statistics using Python as a pedagogical and practical resource. It covers topics such as variability analysis, probability models, distribution functions, statistical inference, bootstrapping, multivariate analysis, time series analysis, and data analytic methods. Numerous examples and case studies are included, and a custom Python package is available for download. The text is suitable for advanced undergraduate and graduate courses in data science, industrial statistics, physical and social sciences, and engineering.

Format: Hardback
Length: 438 pages
Publication date: 01 September 2022
Publisher: Birkhauser Verlag AG

This groundbreaking textbook offers comprehensive material for a course on modern statistics, seamlessly integrating Python as a pedagogical and practical resource. Drawing from extensive teaching and research experiences in diverse applied and industrial settings, the authors have meticulously crafted the text to strike a perfect balance between theory and practical applications. Numerous examples and case studies are interwoven throughout, providing a rich context for understanding statistical concepts. In-depth Python applications are illustrated with clarity, allowing students to reproduce these examples and explore further. Additionally, a custom Python package is made available for download, enabling students to reproduce these examples and delve into additional exploration.

The first chapters of the text delve into the analysis of variability, probability models, and distribution functions. The authors then introduce statistical inference and bootstrapping, exploring variability in multiple dimensions and regression models. The text proceeds to cover sampling techniques for estimating finite population quantities and time series analysis and prediction, culminating in two chapters on modern data analytic methods.

Each chapter is accompanied by exercises, data sets, and applications to reinforce learning. Modern Statistics: A Computer-Based Approach with Python is designed for a one- or two-semester advanced undergraduate or graduate course. Due to its foundational nature, the text can be seamlessly integrated into programs that require data analysis in their curriculum, including courses in data science, industrial statistics, physical and social sciences, and engineering.

Researchers, practitioners, and data scientists alike will find this textbook to be a valuable resource, thanks to the numerous applications and case studies presented. A closely related textbook, Industrial Statistics: A Computer-Based Approach with Python, also offers comprehensive coverage of topics such as statistical process control, quality assurance, and reliability engineering.

In summary, Modern Statistics: A Computer-Based Approach with Python is a groundbreaking textbook that revolutionizes the teaching and learning of modern statistics. By seamlessly integrating Python as a pedagogical tool, it provides students with a comprehensive and practical understanding of statistical concepts and applications. With its rich examples, case studies, and customizable Python package, it equips students with the skills and knowledge needed to excel in today's data-driven world. Whether you are a student, researcher, or practitioner, this textbook is an essential resource for advancing your statistical knowledge and expertise.

Weight: 857g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031075650
Edition number: 1st ed. 2022