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Modern Statistics - A Computer-Based Approach with Python (Hardcover, 1st ed. 2022)
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Modern Statistics - A Computer-Based Approach with Python (Hardcover, 1st ed. 2022)
Series: Statistics for Industry, Technology, and Engineering
Expected to ship within 9 - 15 working days
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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. The first chapters of the text focus on analyzing
variability, probability models, and distribution functions. Next,
the authors introduce statistical inference and bootstrapping, and
variability in several dimensions and regression models. The text
then goes on to cover sampling for estimation of finite population
quantities and time series analysis and prediction, concluding with
two chapters on modern data analytic methods. Each chapter includes
exercises, data sets, and applications to supplement learning.
Modern Statistics: A Computer-Based Approach with Python is
intended for a one- or two-semester advanced undergraduate or
graduate course. Because of the foundational nature of the text, it
can be combined with any program requiring data analysis in its
curriculum, such as courses on data science, industrial statistics,
physical and social sciences, and engineering. Researchers,
practitioners, and data scientists will also find it to be a useful
resource with the numerous applications and case studies that are
included. A second, closely related textbook is titled Industrial
Statistics: A Computer-Based Approach with Python. It covers topics
such as statistical process control, including multivariate
methods, the design of experiments, including computer experiments
and reliability methods, including Bayesian reliability. These
texts can be used independently or for consecutive courses. The
mistat Python package can be accessed at
https://gedeck.github.io/mistat-code-solutions/ModernStatistics/
"In this book on Modern Statistics, the last two chapters on modern
analytic methods contain what is very popular at the moment,
especially in Machine Learning, such as classifiers, clustering
methods and text analytics. But I also appreciate the previous
chapters since I believe that people using machine learning methods
should be aware that they rely heavily on statistical ones. I very
much appreciate the many worked out cases, based on the
longstanding experience of the authors. They are very useful to
better understand, and then apply, the methods presented in the
book. The use of Python corresponds to the best programming
experience nowadays. For all these reasons, I think the book has
also a brilliant and impactful future and I commend the authors for
that." Professor Fabrizio RuggeriResearch Director at the National
Research Council, ItalyPresident of the International Society for
Business and Industrial Statistics (ISBIS)Editor-in-Chief of
Applied Stochastic Models in Business and Industry (ASMBI)
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