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An introduction to statistics with P...

Haslwanter, Thomas.

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An introduction to statistics with Python = with applications in the life sciences /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to statistics with Python/ by Thomas Haslwanter.
Reminder of title:	with applications in the life sciences /
Author:	Haslwanter, Thomas.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvii, 278 p. :ill. (some col.), digital ;25 cm.
[NT 15003449]:	Part I: Python and Statistics -- Why Statistics? -- Python -- Data Input -- Display of Statistical Data -- Part II: Distributions and Hypothesis Tests -- Background -- Distributions of One Variable -- Hypothesis Tests -- Tests of Means of Numerical Data -- Tests on Categorical Data -- Analysis of Survival Times -- Part III: Statistical Modelling -- Linear Regression Models -- Multivariate Data Analysis -- Tests on Discrete Data -- Bayesian Statistics -- Solutions -- Glossary -- Index.
Contained By:	Springer eBooks
Subject:	Mathematical statistics - Data processing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-28316-6
ISBN:	9783319283166

An introduction to statistics with Python = with applications in the life sciences /
Haslwanter, Thomas.

An introduction to statistics with Pythonwith applications in the life sciences /[electronic resource] :by Thomas Haslwanter. - Cham :Springer International Publishing :2016. - xvii, 278 p. :ill. (some col.), digital ;25 cm. - Statistics and computing,1431-8784. - Statistics and computing..

Part I: Python and Statistics -- Why Statistics? -- Python -- Data Input -- Display of Statistical Data -- Part II: Distributions and Hypothesis Tests -- Background -- Distributions of One Variable -- Hypothesis Tests -- Tests of Means of Numerical Data -- Tests on Categorical Data -- Analysis of Survival Times -- Part III: Statistical Modelling -- Linear Regression Models -- Multivariate Data Analysis -- Tests on Discrete Data -- Bayesian Statistics -- Solutions -- Glossary -- Index.

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.

ISBN: 9783319283166

Standard No.: 10.1007/978-3-319-28316-6doiSubjects--Topical Terms:

532521
Mathematical statistics
--Data processing.

LC Class. No.: QA276.4 / .H38 2016

Dewey Class. No.: 519.50285

An introduction to statistics with Python = with applications in the life sciences /
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245 1 3 $a An introduction to statistics with Python $h [electronic resource] : $b with applications in the life sciences / $c by Thomas Haslwanter.
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300 $a xvii, 278 p. : $b ill. (some col.), digital ; $c 25 cm.
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505 0 $a Part I: Python and Statistics -- Why Statistics? -- Python -- Data Input -- Display of Statistical Data -- Part II: Distributions and Hypothesis Tests -- Background -- Distributions of One Variable -- Hypothesis Tests -- Tests of Means of Numerical Data -- Tests on Categorical Data -- Analysis of Survival Times -- Part III: Statistical Modelling -- Linear Regression Models -- Multivariate Data Analysis -- Tests on Discrete Data -- Bayesian Statistics -- Solutions -- Glossary -- Index.
520 $a 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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