Download An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing), by Thomas Haslwanter
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An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing), by Thomas Haslwanter
Download An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing), by Thomas Haslwanter
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Review
“This book is a timely addition designed to bridge the gap between statisticians/computer scientists and experimentalists (biologists, physicists, medical doctors) by focussing on solutions to practical problems … . the book also provides hands-on examples and exercises for a better understanding (for which the solutions are included at the end of the book). This approach makes the book appealing to a wide audience ranging from undergraduates in various subjects to established researchers looking for a focused set of answers.†(Irina Ioana Mohorianu, zbMATH 1357.92001, 2017)
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From the Back Cover
This textbook provides an introduction to the free software 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. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.
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Product details
Series: Statistics and Computing
Paperback: 278 pages
Publisher: Springer; Softcover reprint of the original 1st ed. 2016 edition (May 31, 2018)
Language: English
ISBN-10: 3319803239
ISBN-13: 978-3319803234
Product Dimensions:
6.1 x 0.6 x 9.2 inches
Shipping Weight: 15.4 ounces (View shipping rates and policies)
Average Customer Review:
3.1 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#214,294 in Books (See Top 100 in Books)
Good content, but the "Kindle" version is nothing more than a PDF file. Very disappointed!
Dr Haslwanter was kind enough to provide me with an early copy of his book, which I found to be immensely helpful. The book provides a great overview of Python tools for hypothesis testing, probability distributions, common statistical tests, and statistical modeling. It even includes a chapter on Bayesian analysis! The book provide elegant code that applies the statistical methods to scenarios in the biological sciences which I found especially helpful. It is a very thoughtful and well written book, and fills a needed gap in the scientific python literature.
I have been teaching probability and statistics for many years and I started reading this ebook to learn Python. It did help with Python, but I couldn’t believe what I was reading when I got to the ‘statistics’ part. Section 5.2.2: “a random variate x is a particular outcome of a random variable Xâ€, Sect. 5.2.3: “The PDF also defines the expected value E[X] of a continuous distribution of Xâ€; “If the experiment has been designed correctly, the sample mean should converge to the expected value as more and more samples are included in theanalysisâ€; Sect. 6.1.3: for measurements that cannot be negative, which is usually the case, we can infer that the data have a skewed distribution if the standard deviation is more than half the meanâ€. I stopped reading, there is probably more of this nonsense. Do not read the part on statistics, you will only learn fables. The statistics part would make an undergrad students fail. Springer had a high reputation a few decades ago, now they really are publishing rubbish.
"This book is a timely addition designed to bridge the gap between statisticians/computer scientists and experimentalists (biologists, physicists, medical doctors) by focussing on solutions to practical problems … . the book also provides hands-on examples and exercises for a better understanding (for which the solutions are included at the end of the book). This approach makes the book appealing to a wide audience ranging from undergraduates in various subjects to established researchers looking for a focused set of answers."
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