A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

Autor/inn/en	Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard
Titel	A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students
Quelle	In: Journal of Education for Business, 85 (2010) 5, S.258-263 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0883-2323
Schlagwörter	Regression (Statistics); Classification; Probability; Comparative Analysis; Higher Education; Prediction; Predictor Variables; Academic Achievement; Mathematics Achievement; Sample Size + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Classification system; Klassifikation; Klassifikationssystem; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Hochschulbildung; Hochschulsystem; Hochschulwesen; Vorhersage; Prädiktor; Schulleistung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz
Abstract	The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those obtained previously, by re-examining the data using neural networks and classification trees, from Enterprise Miner, the SAS data mining package, which can provide a prediction of the dependent variable for all cases in the data set including those with missing values. (Contains 2 figures and 2 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10