The paper presents a comparison between two forecasting methods, the first one based on a multiple regression model and the second one based on a connecionist model, applied to rhe problem of choosing bachelor course. The sample is a set of 250 students enrolled at the faculty of Economics of University of Padua in the academic year 1995/96. We investigated the relation between the explanatory variables (scored obtained at the admission test to the course, schoolleaving scores, and others) and the number of passed exams together with the evaluation in a unique variable. This variable allow to characterize three levels of performance. The comparison between the observed and estimated levels, carried out by making use of distribution free tests, has highlighted both substantial homogeneity of results and interesting peculiarities.

Confronto tra un modello di regressione multipla e un modello connessionista per la previsione della prestazione universitaria

PASINI, Margherita;
2001-01-01

Abstract

The paper presents a comparison between two forecasting methods, the first one based on a multiple regression model and the second one based on a connecionist model, applied to rhe problem of choosing bachelor course. The sample is a set of 250 students enrolled at the faculty of Economics of University of Padua in the academic year 1995/96. We investigated the relation between the explanatory variables (scored obtained at the admission test to the course, schoolleaving scores, and others) and the number of passed exams together with the evaluation in a unique variable. This variable allow to characterize three levels of performance. The comparison between the observed and estimated levels, carried out by making use of distribution free tests, has highlighted both substantial homogeneity of results and interesting peculiarities.
2001
Multiple regression; Connectionist model; contingenty coefficient; performance
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/303365
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