Missteps in Multiple Regression Student Projects: Beyond Association-Not-Causation
作者: Marlene A. Smith
作者单位: 1Marlene Smith is Associate Professor of Quantitative Methods, the Business School, University of Colorado Denver, Campus Box 165, PO Box 173364, Denver, CO, 80217-3364. The author thanks the two reviewers and the editors for their thoughtful suggestions.
刊名: The American Statistician, 2011, Vol.65 (3), pp.190-197
来源数据库: Taylor & Francis Journal
DOI: 10.1198/tast.2011.11075
关键词: Active learningClassroom demonstrationsOnline educationProblem-based learningStatistics educationTeaching statistics
原始语种摘要: This article describes common yet subtle errors that students make in self-designed multiple regression projects, based on experiences in a graduate business statistics course. Examples of common errors include estimating algebraic identities, overlooking suppression, and misinterpreting regression coefficients. Advice is given to instructors about helping students anticipate and avoid these common errors; recommended tactics include extensive written guidelines supplemented with in-class active-learning exercises. Several examples using real data are provided. Brief mention is made of incorporating these activities into online courses. Because self-designed student projects require significant effort from students and faculty, anticipating these common errors, and helping students...
全文获取路径: Taylor & Francis  (合作)
影响因子:0.976 (2012)

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