103 resultados para Multiple regression


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This research aims at analyzing the indicators of impact and relevance - total of published articles, average of citations, total number of citations and index h of the most productive researchers in the Metric Studies field, within periods of the Scopus base by means of a correlation study, determining the best equation of regression of index h due to the total of citations, as well as to all other indicators under analysis. As research procedure, we used the search terms bibliometr* OR scientometr* OR infometr* OR webometr* OR informetr* OR webmemetr* OR paentometr*, obtaining 36 researchers as the most productive ones. For each indicator, the following descriptive statistics were calculated: maximum, minimum, average, standard deviation and coefficient of variation. The coefficient of correlation of Pearson was calculated and adjusted to the equation of regression of index h due to the total of citations. The equation of multiple regression was identified, from index h due to the other indicators. Concluding, we highlight the need for a matching of such indicators to broadly describe a researcher's multifaceted profile, seeing the complementarity of information provided by the indicators of productivity and impact, from distinctive nature.

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This clinical study was conducted to correlate the levels of endotoxins and bacterial counts found in primary endodontic infection with the volume of periapical bone destruction determined by cone-beam computed tomography (CBCT) analysis. Moreover, the levels of bacteria and endotoxins were correlated with the development of clinical features. Twenty-four root canals with primary endodontic disease and apical periodontitis were selected. Clinical features such as pain on palpation, pain on percussion, and previous episode of pain were recorded. The volume (cubic millimeters) of periapical bone destruction was determined by CBCT analysis. Endotoxins and bacterial samplings were collected by using sterile/apyrogenic paper points. Endotoxins were quantified by using limulus amebocyte lysate assay (KQCL test), and bacterial count (colony-forming units [CFU]/mL) was determined by using anaerobic culture techniques. Data were analyzed by Pearson correlation and multiple logistic regression (P < .05). Endotoxins and bacteria were detected in 100% of the root canal samples (24 of 24), with median values of 10.92 endotoxin units (EU)/mL (1.75-128 EU/mL) and 7.5 × 10(5) CFU/mL (3.20 × 10(5)-8.16 × 10(6) CFU/mL), respectively. The median volume of bone destruction determined by CBCT analysis was 100 mm(3) (10-450 mm(3)). The multiple regression analysis revealed a positive correlation between higher levels of endotoxins present in root canal infection and larger volume of bone destruction (P < .05). Moreover, higher levels of endotoxins were also correlated with the presence of previous pain (P < .05). Our findings revealed that the levels of endotoxins found in root canal infection are related to the volume of periapical bone destruction determined by CBCT analysis. Moreover, the levels of endotoxin are related to the presence of previous pain.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Metabolic syndrome (MetS) is often accompanied by pro-oxidative and pro-inflammatory processes. Lifestyle modification (LiSM) may act as primary treatment for these processes. This study aimed to elucidate influencing factors on changes of malondialdehyde (MDA) and C-reactive protein (CRP) concentrations after a LiSM intervention. Sixty subjects (53 yrs, 84% women) clinically approved to attend a 20 weeks LiSM-program were submitted to weekly nutritional counseling and physical activities combining aerobic (3 times/week) and resistance (2 times/week) exercises. Before and after intervention they were assessed for anthropometric, clinical, cardiorespiratory fitness test (CRF) and laboratory markers. Statistical analyses performed were multiple regression analysis and backward stepwise with p<0.05 and R(2) as influence index. LiSM was responsible for elevations in CRF, healthy eating index (HEI), total plasma antioxidant capacity (TAP) and HDL-C along with reductions in waist circumference measures and MetS (47-40%) prevalence. MDA and CRP did not change after LiSM, however, we observed that MDA concentrations were positively influenced (R(2)=0.35) by fasting blood glucose (β=0.64) and HOMA-IR (β=0.58) whereas CRP concentrations were by plasma gamma-glutamyltransferase activity (β=0.54; R(2)=0.29). Pro-oxidant and pro-inflammatory states of MetS can be attenuated after lifestyle modification if glucose metabolism homeostasis were recovered and if liver inflammation were reduced, respectively.

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Introduction: The quality of life of dentists has worsened over time, due to increased risks of labor and competitiveness in the labor market. However, there are only few studies about it. Objective: Investigate the perception of quality of life of dentists working in public service. Methods: In order to accomplish that, a cross-sectional study survey was conducted with 52 dentists of the professional permanent staff of the Municipal Health Service. The data were collected through structured questionnaire, validated, self-administered, proposed by the World Health Organization (WHO) in its shortened version, WHOQOL-Bref. Descriptive statistics and multiple regression were conducted, taking 95% as the confidence interval that characterizes the sample and the calculation of scores for each domain. Results: There was predominance of female subjects (76.9%) the majority of them aged 25 to 35 years (48.7%). Most professionals consider their quality of life good (82.7%), and were satisfied with their health (71.2%). Considering the measures of central tendency and dispersion, the physical domain (13.8) and Environment (13.8) had the lowest mean scores. All areas affected equally poor quality of life of research participants. The facets that showed the lowest values were the physical environment with 39.71 points and 53.92 points to financial resources. Conclusion: The majority of professionals were satisfied with their health and considered their quality of life good.

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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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Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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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

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In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.