Analysis of NPK fertilizers inspected by mapa from 2008 to 2010

O Brasil ocupa o quarto lugar mundial em consumo de fertilizantes; dentre esses, destaca-se o fertilizante mineral misto como o mais consumido no País. Depois da atualização da legislação brasileira de fertilizantes em 2004, muitos produtores desses insumos afirmam que houve aumento excessivo no rigor da legislação em relação à fiscalização dos teores de nutrientes neles contidos. Dentro desse contexto, os objetivos deste trabalho foram aplicar técnicas de análise estatística exploratória e descritiva e de regressão logística aos dados de análises fiscais de fertilizantes realizadas pelo Ministério da Agricultura, Pecuária e Abastecimento (MAPA), nos anos de 2008 a 2010, buscando indicadores da contribuição das fontes de variação referentes aos fatores estabelecimento, formulação e laboratório para a variação total dos resultados; e verificar se os níveis de tolerância estabelecidos pelo MAPA estão sendo praticados. Para tanto, esses dados, separados por estabelecimento, formulação, laboratório, especificação granulométrica e período foram submetidos à análise descritiva, seguida de regressão logística. A regressão logística demonstrou que, para N e P analisados pelo laboratório com maior número de observações, as variáveis "estabelecimento" e "formulação" influem no resultado final da análise, dentro ou fora da garantia, enquanto para K, analisado pelo mesmo laboratório, apenas a variável "formulação" influi nesses resultados.Brazil is the fourth largest consumer of fertilizers in the world. Among these, bulk blends fertilizers are the most consumed in the country. After updating of Brazilian legislation in regard to fertilizers in 2004, many producers claim that there was an excessive increase in the strictness of legislation concerning inspection of nutrient levels in those products. Within this context, the objective of this study was to provide an exploratory and descriptive statistical analysis to the data gathered by MAPA (Ministry of Agriculture) from the inspection of the fertilizer industry from the years 2008 to 2010, looking for the most important source of variation, including producer, formula, and laboratory, and verifying if the minimum requirements established by law are being practiced. These data, separated by producer, formula, laboratory, particle size, and period, were subjected to descriptive analysis, followed by logistic regression. Logistic regression showed that for nitrogen and phosphorus analyzed by the laboratory with the greatest number of observations, the "producer" and "formula" variables affect the final results (not-in-conformity or in-conformity), whereas for potassium analyzed by the same laboratory, only the "formula" variable affected the results

Partition clustering of High Dimensional Low Sample Size data based on P-Values

Doctor of PhilosophyDepartment of StatisticsHaiyan WangThis thesis introduces a new partitioning algorithm to cluster variables in high dimensional low sample size (HDLSS) data and high dimensional longitudinal low sample size (HDLLSS) data. HDLSS data contain a large number of variables with small number of replications per variable, and HDLLSS data refer to HDLSS data observed over time. Clustering technique plays an important role in analyzing high dimensional low sample size data as is seen commonly in microarray experiment, mass spectrometry data, pattern recognition. Most current clustering algorithms for HDLSS and HDLLSS data are adaptations from traditional multivariate analysis, where the number of variables is not high and sample sizes are relatively large. Current algorithms show poor performance when applied to high dimensional data, especially in small sample size cases. In addition, available algorithms often exhibit poor clustering accuracy and stability for non-normal data. Simulations show that traditional clustering algorithms used in high dimensional data are not robust to monotone transformations. The proposed clustering algorithm PPCLUST is a powerful tool for clustering HDLSS data, which uses p-values from nonparametric rank tests of homogeneous distribution as a measure of similarity between groups of variables. Inherited from the robustness of rank procedure, the new algorithm is robust to outliers and invariant to monotone transformations of data. PPCLUSTEL is an extension of PPCLUST for clustering of HDLLSS data. A nonparametric test of no simple effect of group is developed and the p-value from the test is used as a measure of similarity between groups of variables. PPCLUST and PPCLUSTEL are able to cluster a large number of variables in the presence of very few replications and in case of PPCLUSTEL, the algorithm require neither a large number nor equally spaced time points. PPCLUST and PPCLUSTEL do not suffer from loss of power due to distributional assumptions, general multiple comparison problems and difficulty in controlling heterocedastic variances. Applications with available data from previous microarray studies show promising results and simulations studies reveal that the algorithm outperforms a series of benchmark algorithms applied to HDLSS data exhibiting high clustering accuracy and stability

Análise de formulações NPK fiscalizadas pelo mapa, de 2008 a 2010

O Brasil ocupa o quarto lugar mundial em consumo de fertilizantes; dentre esses, destaca-se o fertilizante mineral misto como o mais consumido no País. Depois da atualização da legislação brasileira de fertilizantes em 2004, muitos produtores desses insumos afirmam que houve aumento excessivo no rigor da legislação em relação à fiscalização dos teores de nutrientes neles contidos. Dentro desse contexto, os objetivos deste trabalho foram aplicar técnicas de análise estatística exploratória e descritiva e de regressão logística aos dados de análises fiscais de fertilizantes realizadas pelo Ministério da Agricultura, Pecuária e Abastecimento (MAPA), nos anos de 2008 a 2010, buscando indicadores da contribuição das fontes de variação referentes aos fatores estabelecimento, formulação e laboratório para a variação total dos resultados; e verificar se os níveis de tolerância estabelecidos pelo MAPA estão sendo praticados. Para tanto, esses dados, separados por estabelecimento, formulação, laboratório, especificação granulométrica e período foram submetidos à análise descritiva, seguida de regressão logística. A regressão logística demonstrou que, para N e P analisados pelo laboratório com maior número de observações, as variáveis "estabelecimento" e "formulação" influem no resultado final da análise, dentro ou fora da garantia, enquanto para K, analisado pelo mesmo laboratório, apenas a variável "formulação" influi nesses resultados

What do female students in middle and high schools think about computer science majors in Brasilia, Brazil? : a survey in 2011 and 2019

Research Full Paper - Computer Science majors lack gender diversity in Brasília, Brazil. Women are an underrepresented minority group in these majors. At the University of Brasilia, one of the top ten universities in Brazil, female undergraduate students account for less than 15% of the students in the Department of Computer Science. In an effort to understand the lack of interest in Computer Science majors among women, this paper addresses the following research questions: 1)Are female students in high school aware that Computer Science majors are predominantly male? 2)Are families of girls from Brasília supportive of their enrolment in Computer Science majors? 3)Do girls from Brasília think that Computer Science majors need a lot of Math? 4)Do female students in high school think that it is difficult to get a job in the field of Computing, with a good salary, and sufficient leisure time? and 5)Which factors influence a female student's choice of a Computer Science major? We devised a questionnaire and applied it to female students in middle and high school on two occasions, in October 2011 (1391 responses) and in July 2019 (429 responses). This paper presents the analysis of the data from the responses, which indicates that the girls' perceptions of Computing have not changed in those years