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OverviewThis 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. Full Product DetailsAuthor: Thomas HaslwanterPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2016 Dimensions: Width: 15.50cm , Height: 1.60cm , Length: 23.50cm Weight: 4.511kg ISBN: 9783319803234ISBN 10: 3319803239 Pages: 278 Publication Date: 30 May 2018 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPart 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.ReviewsThis 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) Author InformationThomas Haslwanter is a Professor at the Department of Medical Engineering of the University of Applied Sciences Upper Austria in Linz, and lecturer at the ETH Zurich in Switzerland. He also worked as a researcher at the University of Sydney, Australia and the University of Tuebingen, Germany. He has extensive experience in medical research, with a focus on the diagnosis and treatment of vertigo and dizziness and on rehabilitation. After 15 years of extensive use of Matlab, he discovered Python, which he now uses for statistical data analysis, sound and image processing, and for biological simulation applications. He has been teaching in an academic environment for more than 10 years. Tab Content 6Author Website:Countries AvailableAll regions |
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