Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering)

Book Details:

• Author: Ron S. Kenett

• Brand: Birkhauser

• Edition: 1st ed. 2022

• Binding: Paperback

• Format: Import

• Number of Pages: 438

• Release Date: 05-10-2023

• EAN: 9783031075681

• Package Dimensions: 9.3 x 6.1 x 1.1 inches

• Languages: English

About The Book:

Modern Statistics: A Computer-Based Approach with Python offers an innovative and practical approach to modern statistics. The textbook is designed for a one- or two-semester advanced undergraduate or graduate course, with a special focus on integrating Python as both a pedagogical and practical tool. Drawing from years of teaching and research experience, the authors strike an ideal balance between theoretical foundations and hands-on applications.

The text begins by addressing core statistical concepts such as variability, probability models, and distribution functions. It progresses to introduce statistical inference, bootstrapping, multi-dimensional variability, and regression models. Further topics include sampling for finite population estimation, time series analysis, and modern data analytic methods.

What sets this book apart is its comprehensive use of Python throughout. Numerous examples, case studies, and Python applications are provided in detail. A custom Python package, mistat, is available for download, enabling students to reproduce the examples and explore additional exercises and applications.

This textbook is well-suited for courses in data science, industrial statistics, physical and social sciences, engineering, and any field requiring data analysis. In addition to its use in academia, it serves as a valuable resource for researchers, practitioners, and data scientists, offering numerous real-world applications.

Modern Statistics is complemented by a second book, Industrial Statistics: A Computer-Based Approach with Python, which covers industrial topics such as statistical process control, experimental design, and reliability methods, including Bayesian approaches.

Special Features:

• Comprehensive coverage of core statistical concepts with a focus on Python.

• Includes exercises, case studies, and applications to reinforce learning.

• Custom Python package available for download.

• Ideal for advanced undergraduate or graduate students, and useful for researchers and practitioners.