Overview
- Provides an introduction to Python for statistical data analysis
- Covers common statistical tests and various applications, including their implementation and working solutions in Python
- Features a new chapter on finding patterns in data, also in time series, and useful programming tools
Part of the book series: Statistics and Computing (SCO)
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Table of contents (14 chapters)
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Front Matter
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Python and Statistics
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Front Matter
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Distributions and Hypothesis Tests
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Front Matter
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Statistical Modeling
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Front Matter
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Back Matter
Keywords
- Python
- statistical tests
- data analysis
- statistical methods
- programming tools
- applications in the life sciences
- Python source code
- introductory statistics
- data visualization
- hypothesis tests
- survival times
- patterns in data
- time series
- regression
- Bayesian statistics
- generalized linear models
- statistical modelling
- alternative to R
About this book
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.
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: An Introduction to Statistics with Python
Book Subtitle: With Applications in the Life Sciences
Authors: Thomas Haslwanter
Series Title: Statistics and Computing
DOI: https://doi.org/10.1007/978-3-030-97371-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-97370-4Published: 16 November 2022
eBook ISBN: 978-3-030-97371-1Published: 15 November 2022
Series ISSN: 1431-8784
Series E-ISSN: 2197-1706
Edition Number: 2
Number of Pages: XVI, 336
Number of Illustrations: 25 b/w illustrations, 131 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Statistical Theory and Methods, Statistics, general, Biostatistics, Data Structures and Information Theory, Artificial Intelligence