139 resultados para Multiple Correspondence Analysis


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The relationships between the spatial and temporal variations in the abundance of the shrimp Nematopalaemon schmitti and water temperature, salinity, and texture and organic-matter content of the sediment, were analysed in Ubatumirim, Ubatuba and Mar Virado bays on the northern coast of São Paulo, Brazil. Sampling was carried out monthly, from January 1998 through December 1999, from a shrimp boat equipped with double-rig nets, along six transects in each bay. In total, 2 116 specimens of N. schmitti were caught. Their distribution differed among bays, transects and seasons (ANOVA, p < 0.05). Highest total abundance was found in areas of high organicmatter content, in substrate composed mainly of very fine sand and silt and clay, and during winter and autumn. Although multiple regression analysis showed no significant relationship (p > 0.05), observations suggest that water tempera ture, sediment texture, organic-matter content, and the presence of biodetritus and plant fragments, provided favourable environmental conditions for the establishment of N. schmitti in the region.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model