The no-tillage system participatory quality index aims to evaluate the quality and efficiency of soil management under no-tillage systems and consists of a weighted sum of eight indicators: intensity of crop rotation, diversity of crop rotation, persistence of crop residues in the soil surface, frequency of soil tillage, use of agricultural terraces, evaluation of soil conservation, balance of soil fertilization and time of adoption of the no-tillage system. The aim of this study was to assess the extent to which these indicators correlate with the no-tillage system participatory quality index and to characterize the farmers who participated in the research. The data used were provided by ITAIPU Binacional for the indicators of the no-tillage system participatory quality index II. Descriptive analyses were performed, and the Pearson correlation coefficient between the index and each indicator was calculated. To assess the relationship between the indicators and the farmers’ behavior toward the indicators, principal component analysis and cluster analysis were performed. Although all correlations are significant at p-value ≤ 0.05, some correlations are weak, indicating a need for improvement of the index. The principal component analysis identified three principal components, which explained 66% of the variability of the data, and the cluster analysis separated the 121 farmers into five groups. It was verified that the no-tillage system participatory quality index II has some limitations and should therefore be reevaluated to increase its efficiency as an indicator of the quality of the no-tillage system.

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Many methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the SPDE approach that requires the creation of a mesh overlying the study area and all the obtained results depend on it. The impact of the mesh on inference and prediction is investigated through simulations. As there is no formal procedure to specify it, we investigate a guideline to create an optimal mesh

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This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.

One of the key features in regression models consists in selecting appropriate characteristics that explain the behavior of the response variable, in which stepwise-based procedures occupy a prominent position. In this paper we performed several simulation studies to investigate whether a specific stepwise-based approach, namely Strategy A, properly selects authentic variables into the generalized additive models for location, scale and shape framework, considering Gaussian, zero inflated Poisson and Weibull distributions. Continuous (with linear and nonlinear relationships) and categorical explanatory variables are considered and they are selected through some goodness-of-fit statistics. Overall, we conclude that the Strategy A greatly performed.

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RESUMO:OBJETIVO: Analisar a qualidade de vida e renda dos munícipes da cidade de Presidente Epitácio, São Paulo, por meio de métodos estatísticos. MÉTODOS: O estudo foi realizado com base em um questionário aplicado aos moradores de Presidente Epitácio, São Paulo, no ano de 2008, como parte da pesquisa desenvolvida por Santos (2010), na qual foram considerados os 47 setores censitários do município definidos pelo Instituto Brasileiro de Geografia e Estatística (IBGE) para o censo demográfico de 2000. Para a análise estatística, foram utilizados os modelos de regressão linear simples e múltipla e, posteriormente, foi considerada a dependência espacial das observações por meio do modelo espacial autorregressivo misto (Spatial AutoRegressive -SAR) e do modelo de erro espacial (Conditional AutoRegressive -CAR). Todos os procedimentos estatísticos e computacionais foram realizados com auxílio dos programas Minitab 17 e GeoDa. RESULTADO: Após uma análise exploratória prévia e ajuste do modelo de regressão linear clássico, verificou-se a necessidade da inclusão da dependência espacial nas variáveis em estudo. Dentre os modelos espaciais ajustados, verificou-se, por meio de critérios estatísticos, que o modelo CAR é o mais adequado para descrever o problema considerado. O modelo foi capaz de apontar que a região mais ao sul da cidade de Presidente Epitácio, São Paulo, possui qualidade de vida mais baixa, ao passo que as regiões norte e nordeste do município possuem qualidade de vida mais alta. CONCLUSÕES: A metodologia aplicada se mostrou uma poderosa ferramenta no que tange à distinção de áreas que apresentam maior ou menor qualidade de vida no município de Presidente Epitácio, São Paulo. Com isto, esta pesquisa pode oferecer subsídios para a criação de políticas públicas específicas para cada uma das regiões (setores censitários) da cidade em estudo. PALAVRAS-CHAVE:Regressão linear. Qualidade de vida. Políticas públicas.