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Demonstrating how the application of two selected statistical methods to COVID-19 data can render the research component of an MSc Economics Degree program at a Caribbean University more meaningful and understandable to students who score high failure rates on account of not having a sound background in statistics

Authors: Linda Hewitt; Roxanne Brizan-St. Martin;

Demonstrating how the application of two selected statistical methods to COVID-19 data can render the research component of an MSc Economics Degree program at a Caribbean University more meaningful and understandable to students who score high failure rates on account of not having a sound background in statistics

Abstract

Big data is a welcomed addition to the conventional field of the Sciences, coming at a time of increased volumes, having additional collection methods, diverse ways of manipulating and processing data using a variety of tools for undertaking analyses, graphical representation, and dissemination. New experiences confronting the global environment serve to widen the scope of pedagogy enhancing mode of delivery and giving many dimensions to data culture. In the context of COVID-19, the extensiveness of its impact is indicative of far-reaching consequences and data requirement in order to understand and deal with the difficulties that have come about. In this paper, we demonstrate how practical applications of statistical procedures, as applied to the COVID-19 pandemic, can improve teaching and learning in a graduate Economics programme in the Caribbean. The paper provides some possibilities for syllabus construction to better prepare students of Economics for the world of research, academia and policy making using relevant data.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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