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    Promoting Statistical Practice and Collaboration in Developing Countries

    Proposal review

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    Contributor(s)
    Awe, O. Olawale (editor)
    Love, Kim (editor)
    Vance, Eric A. (editor)
    Language
    English
    Show full item record
    Abstract
    "Rarely, but just often enough to rebuild hope, something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance, and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers, or accountants, or nurses, or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change, of course, but ideas are durable, and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy!" —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education, statistical consulting, and collaboration, particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers, scholars, students, and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences, which include practitioners, researchers, students, and statistics educators in developing countries.
    URI
    https://library.oapen.org/handle/20.500.12657/101006
    Keywords
    Statistics; Global Development; Statistical Theory; Statistical Methods; HPD Interval; Statistical Consulting Centers; Undergraduate Statistics Programs; Undergraduate Statistics Students; Pennsylvania State University; Vice Versa; Autoregressive Distributed Lag Model; HPD; Data Science Practice; Data Set; LISA; Statistical Collaboration; Statistical Literacy; SCS; North Central Nigeria; Big Data Modeling; Sample Size Determination; Tertiary Education; Undergraduate Students; Collaborative Statisticians
    DOI
    10.1201/9781003261148
    ISBN
    9781000594355, 9781032198507, 9781000594416, 9781032195551, 9781003261148, 9781000594355
    OCN
    1316708182
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2022
    Grantor
    • University of Colorado - [...]
    Imprint
    Chapman and Hall/CRC
    Classification
    Probability and statistics
    Development studies
    Psychological methodology
    Pages
    634
    Rights
    https://creativecommons.org/licenses/by-nc-nd/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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