We display the first two moment functions of the Logitnormal family of distributions, conveniently described in terms of the Normal mean, m, and the Normal signal-to-noiseratio, m/s, parameters that generate the family. Long neglected on account of the numerical integrations required to compute them, awareness of these moment functions should aid the sensible interpretation of logistic regression statistics and the specification of 'diffuse' prior distributions in hierarchical models, which can be deceiving. We also use numerical integration to compare the correlation between bivariate Logitnormal variables with the correlation between the bivariate Normal variables from which they are transformed

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast.

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Programme for International Student Assessment data from 29 countries was used to measure immigrant school gaps (differences in scores between immigrants and natives) in relation to various potentially correlated factors. Results show that negative gaps are concentrated in the European Union; in the South, they are mainly correlated with school types - academic, intermediate or vocational - and country of origin; and in the North, they remain negative in all model specifications. This suggests a lack of assimilation, in some cases reinforced by educational institutions. Gaps are generally small in English-speaking countries; in the USA and GBR they are influenced by backgroun

The determinants of the transition from lower secondary to upper secondary school of Italian and immigrant teenagers (16-19 age range) were identified joining the European Union Statistics on Income and Living Conditions (EU-SILC) and the Italian Survey on Income and Living Conditions of Families with Immigrants in Italy (IM-SILC) for 2009. A set of individual, family, and contextual characteristics was selected through the Lasso method and a Bayesian approach to explain the choice of upper secondary schooling (yes/no). The transition from the low secondary to upper secondary school showed a complex pattern involving many variables: compared to men, women did not prove to have any differences, many components of income entered the model in a parabolic form, education level and income of parents proved to be very important, as was their occupation. The contextual factors revealed their importance: the latter included the degree of urbanisation, the South macro-region, household tenure status, the amount of optional technological equipment, and so on. Differences between Italians and immigrants disappeared when family background and parental characteristics were taken into account.