2007
DOI: 10.1080/10691898.2007.11889455
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Trashball: A Logistic Regression Classroom Activity

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Cited by 6 publications
(4 citation statements)
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“…Importantly, these studies show that teaching logistic regression helps reinforce linear regression concepts (Morrell & Auer, 2007) and enhance critical and analytical thinking (Brusco, 2022; Li et al., 2018). In contrast to linear regression, binary logistic regression offers unique opportunities for students to learn about out‐of‐sample testing and how to evaluate models using classification performance metrics (accuracy, sensitivity, and specificity).…”
Section: Literature Reviewmentioning
confidence: 94%
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“…Importantly, these studies show that teaching logistic regression helps reinforce linear regression concepts (Morrell & Auer, 2007) and enhance critical and analytical thinking (Brusco, 2022; Li et al., 2018). In contrast to linear regression, binary logistic regression offers unique opportunities for students to learn about out‐of‐sample testing and how to evaluate models using classification performance metrics (accuracy, sensitivity, and specificity).…”
Section: Literature Reviewmentioning
confidence: 94%
“…Literature has shown that logistic regression is among the most popular methodological tools in predictive analytics (Brusco, 2022). Given that more introductory IBA and statistics books are including logistic regression (Morrell & Auer, 2007), one could expect that logistic regression has been taught widely in undergraduate IBA courses. However, our literature review finds limited discussion on curriculum design for those courses.…”
Section: Literature Reviewmentioning
confidence: 99%
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“…The development of Excel spreadsheets to address the aforementioned problems assumes that students have already been exposed to some of the basic principles of logistic regression, such as (i) the difference between relative risk and the odds ratio, specifically noting that the former is a ratio of probabilities rather than odds; (ii) a plot of the logistic function across different values of probability for the dependent variable; (iii) the difference between categorical and continuous independent variables in logistic regression; and (iv) some "toy" examples to introduce basic concepts concerning likelihood and coefficient interpretation. Some good teaching practices for introducing students to logistic regression are available in the literature (Campbell 1998, Morrell andAuer 2007).…”
Section: Introductionmentioning
confidence: 99%