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Modelling gained university credits: mixtures vs quantile regression
Leonardo Grilli, Dept. of Statistics, Computer Science, Applications "G. Parenti", Univ. of Florence
Venerdì 27 novembre 2015, ore 12,00
Aula 202

The talk will consider two recent papers by Grilli, Rampichini and Varriale about modelling credits obtained by university freshmen during the first year, in order to investigate whether the pre-enrolment assessment test is an effective tool to predict student performance. Looking at data from the School of Economics of the University of Florence, it appears that gained credits is a count variable with an irregular distribution and a peak in zero. This pattern represents a challenge in statistical modelling, which is tackled using two distinct approaches: (i) a concomitant variable binomial mixture model, and (ii) a two-part model with a logit specification for the zeros, while positive values are analyzed by quantile regression for counts. The two approaches are applied to the same dataset, discussing issues of estimation and interpretation.

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