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)
