An Introduction to Statistics with Python With Applications in the Life Sciences /

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 cod...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Haslwanter, Thomas (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
Σειρά:Statistics and Computing,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Haslwanter, Thomas.  |e author. 
245 1 3 |a An Introduction to Statistics with Python  |h [electronic resource] :  |b With Applications in the Life Sciences /  |c by Thomas Haslwanter. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a XVII, 278 p. 113 illus., 85 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Statistics and Computing,  |x 1431-8784 
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. . 
650 0 |a Statistics. 
650 0 |a Programming languages (Electronic computers). 
650 0 |a Biostatistics. 
650 0 |a Computer mathematics. 
650 1 4 |a Statistics. 
650 2 4 |a Statistics and Computing/Statistics Programs. 
650 2 4 |a Statistics for Life Sciences, Medicine, Health Sciences. 
650 2 4 |a Biostatistics. 
650 2 4 |a Computational Science and Engineering. 
650 2 4 |a Programming Languages, Compilers, Interpreters. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319283159 
830 0 |a Statistics and Computing,  |x 1431-8784 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-28316-6  |z Full Text via HEAL-Link 
912 |a ZDB-2-SMA 
950 |a Mathematics and Statistics (Springer-11649)