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.
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