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    Learning Statistics with jamovi

    A Tutorial for Beginners in Statistical Analysis

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    Author(s)
    Navarro, Danielle cc
    Foxcroft, David R.
    Language
    English
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    Abstract
    Based on Danielle Navarro’s widely acclaimed and prize-winning book Learning Statistics with R, this elegantly designed textbook offers undergraduate students a thorough and accessible introduction to jamovi, as well as how to get to grips with statistics and data manipulation. Lucid and easy to understand, Learning Statistics with jamovi covers the analysis of contingency tables, t-tests, correlation, regression, ANOVA and factor analysis, while also giving students a firm grounding in descriptive statistics and graphing. It includes learning aids for applying statistical principles using the jamovi interface, as well as embedded data files to accompany the book, and comprehensive chapters on probability theory, sampling and estimation, and null hypothesis testing. Freely available in open access, Learning Statistics with jamovi is an ideal introduction for undergraduate and postgraduate students of psychology, as well as behavioural and health science students and anyone who needs to understand and use statistical analysis in their work.
    URI
    https://library.oapen.org/handle/20.500.12657/97986
    Keywords
    Statistics;jamovi;Data Analysis;Descriptive Statistics;Hypothesis Testing;Psychology and Health Sciences
    DOI
    10.11647/OBP.0333
    ISBN
    9781800649378, 9781800649385, 9781800649392
    Publisher
    Open Book Publishers
    Publisher website
    https://www.openbookpublishers.com/
    Publication date and place
    Cambridge, 2025
    Classification
    Probability and statistics
    Data capture and analysis
    Social research and statistics
    Behaviourism, Behavioural theory
    Pages
    492
    Rights
    https://creativecommons.org/licenses/by-sa/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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