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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
        9781800649392, 9781800649378, 9781800649385
        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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