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ERIC Number: EJ1172824
Record Type: Journal
Publication Date: 2018
Pages: 17
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1051-1970
Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods
Helmreich, James E.; Krog, K. Peter
PRIMUS, v28 n3 p206-222 2018
We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex functions, students discover the sample mean, median, and "p"th quantile by minimizing sums of distances. Students use these metrics to define loss functions for OLS, LAD, and QR, and explore methods to minimize them. We discuss classroom experiences over two semesters. Classroom activities, Maple worksheets, and R demonstration code are available at a companion website.
Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Guides - Classroom - Teacher
Education Level: Higher Education
Audience: Teachers
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: New York