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

✍ Scribed by Thomas Haslwanter (auth.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 285
• Series: Statistics and Computing
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Why Statistics?....Pages 3-4
Python....Pages 5-42
Data Input....Pages 43-49
Display of Statistical Data....Pages 51-71
Front Matter....Pages 73-73
Background....Pages 75-88
Distributions of One Variable....Pages 89-120
Hypothesis Tests....Pages 121-137
Tests of Means of Numerical Data....Pages 139-157
Tests on Categorical Data....Pages 159-173
Analysis of Survival Times....Pages 175-180
Front Matter....Pages 181-181
Linear Regression Models....Pages 183-220
Multivariate Data Analysis....Pages 221-225
Tests on Discrete Data....Pages 227-236
Bayesian Statistics....Pages 237-243
Back Matter....Pages 245-278

✦ Subjects

Statistics and Computing/Statistics Programs;Statistics for Life Sciences, Medicine, Health Sciences;Biostatistics;Computational Science and Engineering;Programming Languages, Compilers, Interpreters