3 resultados para Statistical variance

em Instituto Politécnico do Porto, Portugal


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The development of neonatal intensive care has led to an increase in the prevalence of children with low birth weight and associated morbidity. The objectives of this study are to verify (1) The association between birth weight (BW) and neuromotor performance? (2) Is the neuromotor performance of twins within the normal range? (3) Are intra-pair similarities in neuromotor development of Monozygotic (MZ) and Disygotic (DZ) twins of unequal magnitude? The sample consisted of 191 children (78 MZ and 113 DZ), 8.9+3.1 years of age and with an average BW of 2246.3+485.4g. In addition to gestational characteristics, sports participation and Zurich Neuromotor Assessment (ZNA) were observed at childhood age. The statistical analysis was carried out with software SPSS 18.0, the STATA 10 and the ZNA performance scores. The level of significance was 0.05. For the neuromotor items high intra and inter-investigator reliabilities were obtained (0.793variance. Twins showed elevated percentages of subjects (32.7%<76.9%) with low performance

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In this paper the adequacy and the benefit of incorporating glass fibre reinforced polymer (GFRP) waste materials into polyester based mortars, as sand aggregates and filler replacements, are assessed. Different weight contents of mechanically recycled GFRP wastes with two particle size grades are included in the formulation of new materials. In all formulations, a polyester resin matrix was modified with a silane coupling agent in order to improve binder-aggregates interfaces. The added value of the recycling solution was assessed by means of both flexural and compressive strengths of GFRP admixed mortars with regard to those of the unmodified polymer mortars. Planning of experiments and data treatment were performed by means of full factorial design and through appropriate statistical tools based on analyses of variance (ANOVA). Results show that the partial replacement of sand aggregates by either type of GFRP recyclates improves the mechanical performance of resultant polymer mortars. In the case of trial formulations modified with the coarser waste mix, the best results are achieved with 8% waste weight content, while for fine waste based polymer mortars, 4% in weight of waste content leads to the higher increases on mechanical strengths. This study clearly identifies a promising waste management solution for GFRP waste materials by developing a cost-effective end-use application for the recyclates, thus contributing to a more sustainable fibre-reinforced polymer composites industry.

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Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.