An Introduction to Statistics with Python: With Applications in the Life Sciences

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.

For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.

The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader's immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.

With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.

這本教科書的第二版介紹了Python及其在統計數據分析中的應用。它涵蓋了連續、離散和分類數據的常見統計測試，以及線性回歸分析、生存分析和貝葉斯統計的主題。

在這個新版中，Python、數據輸入和可視化的入門章節已經重新修訂和更新。實驗設計章節已擴展，並引入了在質量控制中常用的置信區間確定程序。該書還新增了一個關於在數據中尋找模式的章節，包括時間序列。一個新的附錄描述了有用的編程工具，如測試工具、代碼庫和GUI。

提供的Python解決方案的工作代碼，以及易於理解的示例，將加強讀者對主題的即時理解。相應的數據集和Python程序也可以在線上獲得。隨著Python生態系統的最新進展，Python已成為科學計算的流行語言，為統計數據分析提供了強大的環境。

這本書主要針對碩士和博士生，並以生命科學和醫學科學為主要例子。由於提供了所需的統計背景，該書也適用於任何想進行統計數據分析的人。

Thomas Haslwanter is a Professor at the School of Medical Engineering and Applied Social Sciences at the University of Applied Sciences Upper Austria in Linz, and lecturer at the ETH Zurich in Switzerland. He also worked as a researcher at the University of Sydney, Australia and the University of Tübingen, Germany. He has extensive experience in medical research, with a focus on the diagnosis and treatment of vertigo and dizziness and on rehabilitation. After 15 years of extensive use of Matlab, he discovered Python, which he now uses for statistical data analysis, sound and image processing, and for biological simulation applications. He has been teaching in an academic environment for more than 15 years.

Thomas Haslwanter是奧地利上奧地利應用科學大學醫學工程與應用社會科學學院的教授，也是瑞士蘇黎世聯邦理工學院的講師。他曾在澳大利亞悉尼大學和德國圖賓根大學擔任研究員。他在醫學研究方面擁有豐富的經驗，專注於眩暈和頭暈的診斷和治療以及康復。在長達15年的Matlab使用經驗後，他發現了Python，現在他使用Python進行統計數據分析、聲音和圖像處理以及生物模擬應用。他已在學術環境中教學超過15年。