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ERIC Number: EJ1384299
Record Type: Journal
Publication Date: 2023
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0020-739X
EISSN: EISSN-1464-5211
Creating Predictive Clothing Size Models for Online Customers
Davidson, Allison; Gundlach, Ellen
International Journal of Mathematical Education in Science and Technology, v54 n4 p614-629 2023
A disadvantage to online clothes shopping is the inability to try on clothing to test the fit. A class project is discussed where students consult with the CEO of an online mensware clothing company to explore ways in which an online clothing customer can be assured of a superior fit by developing statistical models based on a shopper's height and weight to predict measurements needed to create a suit that feels custom-made. The dataset is most amenable to use with students who have previously been exposed to simple linear regression, and can be used to explore multiple regression topics such as interaction terms, influential points, transformations, and polynomial predictors. Discussion points are included for more advanced topics such as canonical correlation, clustering, and dimension reduction.
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; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A