rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: Introduction to Statistics with Python
    sub title: With Applications in the Life Sciences
  name:
    name part: Haslwanter, Thomas.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 2nd ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XVI, 336 pages 156 illustrations, 131 illustrations in color)
  abstract: 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 reader's 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. .
  table of contents: 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.
  note: by Thomas Haslwanter.
  subject:
    topic: Statistics-Computer programs
  subject:
    topic: Statistics
  subject:
    topic: Quantitative research
  subject:
    topic: Biometry
  subject:
    topic: Artificial intelligence-Data processing
  subject:
    topic: Mathematical statistics-Data processing
  subject:
    topic: Statistical Software
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Data Analysis and Big Data
  subject:
    topic: Biostatistics
  subject:
    topic: Data Science
  subject:
    topic: Statistics and Computing
  classification: QA276.4-.45
  classification: 519.50285
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Statistics and Computing
  identifier: 9978-3-030-97371-1
  identifier: 9783030973711
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221115
    record change date: 20240805185314.5
    record identifier: in00004659494