An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter, series editor W. K. Hardle

By:

Haslwanter, Thomas

Contributor(s):

Material type: Text

TextSeries: Scientists and ComputingDescription: xvii, 278 pages : illustrations ; 25 cmISBN:

9783319283159 (hardback)
9783319283166 (eBook)

Subject(s):

DDC classification:

519.50285

Other classification:

519.50285

Summary: 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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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	Junaid Zaidi Library, COMSATS University Islamabad Ground Floor	519.50285 HAS-I (Browse shelf(Opens below))	Available		58086
Books	Junaid Zaidi Library, COMSATS University Islamabad Ground Floor	005.74 KRO-D (Browse shelf(Opens below))	Available		58372

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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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