Modern Statistics: A Computer-Based Approach with Python

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)

這本創新的教科書提供了一門以Python作為教學和實踐資源的現代統計學課程的教材。作者們在多年的教學和應用研究中，根據應用和工業領域的需求精心編寫了這本教材，以提供理論和實際應用的理想平衡。書中融入了許多例子和案例研究，並詳細介紹了Python的應用。讀者可以下載一個自定義的Python套件，以重現這些例子並探索其他應用。

教材的前幾章重點介紹了變異性分析、概率模型和分佈函數。接下來，作者介紹了統計推斷和自助法，以及多維度的變異性和回歸模型。教材還涵蓋了用於估計有限總體數量和時間序列分析和預測的抽樣方法，最後兩章介紹了現代數據分析方法。每章都包含練習題、數據集和應用案例，以輔助學習。

《現代統計學：基於Python的計算機方法》適用於一學期或兩學期的高年級本科生或研究生課程。由於教材的基礎性質，它可以與任何需要在課程中進行數據分析的計劃結合使用，例如數據科學、工業統計、自然科學和社會科學以及工程學等課程。研究人員、從業人員和數據科學家也會發現這本書是一個有用的資源，其中包含了許多應用案例和案例研究。

另一本與之密切相關的教科書名為《工業統計學：基於Python的計算機方法》。它涵蓋了統計過程控制，包括多變量方法，實驗設計，包括計算機實驗和可靠性方法，包括貝葉斯可靠性等主題。這些教材可以獨立使用或連續使用。

可以在https://gedeck.github.io/mistat-code-solutions/ModernStatistics/上訪問mistat Python套件。

「在這本《現代統計學》的書中，現代分析方法的最後兩章包含了目前非常流行的機器學習方法，例如分類器、聚類方法和文本分析。但我也欣賞前面的章節，因為我認為使用機器學習方法的人應該意識到它們在很大程度上依賴於統計方法。我非常欣賞作者們多年的經驗所提供的許多實例，它們對於更好地理解並應用書中介紹的方法非常有用。使用Python符合當今最佳的編程體驗。出於這些原因，我認為這本書也有著光明和有影響力的未來，我對作者們表示讚賞。」

Fabrizio Ruggeri教授
意大利國家研究委員會研究主任
國際工商統計學會（ISBIS）主席
《應用隨機模型在商業和工業中的應用》（ASMBI）主編

Professor Ron Kenett is Chairman of the KPA Group, Israel and Senior Research Fellow at the Samuel Neaman Institute, Technion, Haifa Israel and Professor, University of Turin, Italy. He is an applied statistician combining expertise in academic, consulting and business domains.
Shelemyahu Zacks is a Distinguished Professor emeritus in the Mathematical Sciences department of Binghamton University.He is a Fellow of the IMS, ASA, AAAS and an elected member of the ISI. Professor Zacks has published eleven books and more than 170 journal articles on subjects of design of experiments, statistical process control, statistical decision theory, sequential analysis, reliability and sampling from finite populations. Professor Zacks served as an Editor and Associate Editor of several Statistics and Probability journals.
Dr. Peter Gedeck, a Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. In addition, he teaches data science at the University of Virginia and at statistics.com.

Ron Kenett教授是以色列KPA集團主席，以色列海法Technion的Samuel Neaman研究所高級研究員，也是意大利都靈大學的教授。他是一位應用統計學家，結合學術、咨詢和商業領域的專業知識。

Shelemyahu Zacks教授是賓漢姆頓大學數學科學系的杰出教授。他是IMS、ASA、AAAS的會士，也是ISI的當選會員。Zacks教授在設計實驗、統計過程控制、統計決策理論、序列分析、可靠性和有限人口抽樣等領域發表了11本書和170多篇期刊文章。他曾擔任多個統計學和概率學期刊的編輯和副編輯。

Peter Gedeck博士是Collaborative Drug Discovery的高級數據科學家，專注於開發機器學習算法來預測藥物候選物的生物和物理化學性質。此外，他還在維吉尼亞大學和statistics.com教授數據科學。