Creating Predictive Clothing Size Models for Online Customers

Autor/inn/en	Davidson, Allison; Gundlach, Ellen
Titel	Creating Predictive Clothing Size Models for Online Customers
Quelle	In: International Journal of Mathematical Education in Science and Technology, 54 (2023) 4, S.614-629 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Davidson, Allison) ORCID (Gundlach, Ellen)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0020-739X
DOI	10.1080/0020739X.2022.2040623
Schlagwörter	Internet; Retailing; Prediction; Clothing; Consumer Education; Intervals; Body Composition; Error of Measurement + Suchen Sie Ihr Suchwort? Warenwirtschaft; Vorhersage; Kleidung; Konsumerziehung; Messfehler
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01