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Title
An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter.
Edition
2nd ed.
ISBN
9783030973711 (electronic bk.)
3030973719 (electronic bk.)
9783030973704
3030973700
Publication Details
Cham : Springer, 2022.
Language
English
Description
1 online resource (341 p.).
Item Number
10.1007/978-3-030-97371-1 doi
Call Number
QA276.4
Dewey Decimal Classification
519.50285
Summary
Now in its second edition, this textbook provides an introduction to 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. For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs. The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the readers immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis. With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis. .
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 28, 2022).
Series
Statistics and computing.
I Python and Statistics
1 Introduction
2 Python
3 Data Input
4 Data Display
II Distributions and Hypothesis Tests
5 Basic Statistical Concepts
6 Distributions of One Variable
7 Hypothesis Tests
8 Tests of Means of Numerical Data
9 Tests on Categorical Data
10 Analysis of Survival Times
III Statistical Modelling
11 Finding Patterns in Signals
12 Linear Regression Models
13 Generalized Linear Models
14 Bayesian Statistics
Appendices
A Useful Programming Tools
B Solutions
C Equations for Confidence Intervals
D Web Ressources
Glossary
Bibliography
Index.
