896 resultados para Regression equation


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Studies have shown that an increase in arterial stiffening can indicate the presence of cardiovascular diseases like hypertension. Current gold standard in clinical practice is by measuring the blood pressure of patients using a mercury sphygmomanometer. However, the nature of this technique is not suitable for prolonged monitoring. It has been established that pulse wave velocity is a direct measure of arterial stiffening. However, its usefulness is hampered by the absence of techniques to estimate it non-invasively. Pulse transit time (PTT) is a simple and non-intrusive method derived from pulse wave velocity. It has shown its capability in childhood respiratory sleep studies. Recently, regression equations that can predict PTT values for healthy Caucasian children were formulated. However, its usefulness to identify hypertensive children based on mean PTT values has not been investigated. This was a continual study where 3 more Caucasian male children with known clinical hypertension were recruited. Results indicated that the PTT predictive equations are able to identify hypertensive children from their normal counterparts in a significant manner (p < 0.05). Hence, PTT can be a useful diagnostic tool in identifying hypertension in children and shows potential to be a non-invasive continual monitor for arterial stiffening.

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This study examined the relationship between isokinetic hip extensor/hip flexor strength, 1-RM squat strength, and sprint running performance for both a sprint-trained and non-sprint-trained group. Eleven male sprinters and 8 male controls volunteered for the study. On the same day subjects ran 20-m sprints from both a stationary start and with a 50-m acceleration distance, completed isokinetic hip extension/flexion exercises at 1.05, 4.74, and 8.42 rad.s(-1), and had their squat strength estimated. Stepwise multiple regression analysis showed that equations for predicting both 20-m maximum velocity nm time and 20-m acceleration time may be calculated with an error of less than 0.05 sec using only isokinetic and squat strength data. However, a single regression equation for predicting both 20-m acceleration and maximum velocity run times from isokinetic or squat tests was not found. The regression analysis indicated that hip flexor strength at all test velocities was a better predictor of sprint running performance than hip extensor strength.

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OBJETIVO: comparar medidas de tamanhos dentários, suas reprodutibilidades e a aplicação da equação de regressão de Tanaka e Johnston na predição do tamanho dos caninos e pré-molares em modelos de gesso e digital. MÉTODOS: trinta modelos de gesso foram escaneados para obtenção dos modelos digitais. As medidas do comprimento mesiodistal dos dentes foram obtidas com paquímetro digital nos modelos de gesso e nos modelos digitais utilizando o software O3d (Widialabs). A somatória do tamanho dos incisivos inferiores foi utilizada para obter os valores de predição do tamanho dos pré-molares e caninos utilizando equação de regressão, e esses valores foram comparados ao tamanho real dos dentes. Os dados foram analisados estatisticamente, aplicando-se aos resultados o teste de correlação de Pearson, a fórmula de Dahlberg, o teste t pareado e a análise de variância (p < 0,05). RESULTADOS: excelente concordância intraexaminador foi observada nas medidas realizadas em ambos os modelos. O erro aleatório não esteve presente nas medidas obtidas com paquímetro, e o erro sistemático foi mais frequente no modelo digital. A previsão de espaço obtida pela aplicação da equação de regressão foi maior que a somatória dos pré-molares e caninos presentes nos modelos de gesso e nos modelos digitais. CONCLUSÃO: apesar da boa reprodutibilidade das medidas realizadas em ambos os modelos, a maioria das medidas dos modelos digitais foram superiores às do modelos de gesso. O espaço previsto foi superestimado em ambos os modelos e significativamente maior nos modelos digitais.

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An investigator may also wish to select a small subset of the X variables which give the best prediction of the Y variable. In this case, the question is how many variables should the regression equation include? One method would be to calculate the regression of Y on every subset of the X variables and choose the subset that gives the smallest mean square deviation from the regression. Most investigators, however, prefer to use a ‘stepwise multiple regression’ procedure. There are two forms of this analysis called the ‘step-up’ (or ‘forward’) method and the ‘step-down’ (or ‘backward’) method. This Statnote illustrates the use of stepwise multiple regression with reference to the scenario introduced in Statnote 24, viz., the influence of climatic variables on the growth of the crustose lichen Rhizocarpon geographicum (L.)DC.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Objetivou-se avaliar os efeitos da ingestão diária de quatro níveis de fósforo (8, 12, 15 e 18 g) sobre o metabolismo de macrominerais (P, Ca, Mg, Na, K e S), incluindo a ingestão, a concentração no rúmen, a taxa de passagem do líquido ruminal, a excreção nas fezes e a disponibilidade aparente. Utilizaram-se quatro bubalinos adultos com fístulas ruminais em delineamento quadrado latino (4 × 4) com dieta total constituída de cana-de-açúcar como volumoso (85%) e concentrado formulado com um dos níveis de fósforo. Os níveis de fósforo não ocasionaram diferença significativa na concentração mineral no rúmen de nenhum mineral estudado. A concentração média de fósforo no conteúdo ruminal foi de 0,98% na matéria seca, enquanto o teor de fósforo nas rações variou de 0,12 a 0,34%, comprovando alta reciclagem de fósforo pela saliva. Níveis crescentes de fósforo na dieta, variando de 8 a 18 g/animal/dia, não influenciam as disponibilidades de cálcio e magnésio. Com o nível de fósforo de 15 g/dia, houve melhor utilização do fósforo da dieta. A ingestão de níveis crescentes de fósforo em g/kg0,75 (X) promoveu aumento linear na excreção fecal desse mineral em g/kg0,75 (Y) e baixos valores de disponibilidade do fósforo, que pode ser estimado pela equação Y = 0,03 + 0,610X, o que indica deficiência desse elemento mineral na dieta para o metabolismo animal.

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This article presents an evaluation of the effects of the spouted bed design and operating conditions on system fluiddynamics and process performance during enteric coating of hard gelatine capsules. The design parameters studied were the column diameter (150 mm and 200 mm), the included angle of the conical base, gamma (60 degrees or 40 degrees) and the presence or absence of a Venturi inserted before the inlet air orifice. The process variables studied were the ratio between the feed flow rate of the coating suspension to the spouting gas flow rate (W(s)/W(g)), the mass of capsules loaded to the equipment (M(0)), and the ratio between the Spouting gas flow rate to the gas flow rate at minimum spouting condition (Q/Q(ms)). The response variables were the rate of increase of the capsules mass (K(1)), and the adhesion efficiency (eta). The linear regression equation for the dependent variable K, in terms of the independent variables adequately described the process with an r(2) value of 0.872. Analysis of variance (ANOVA) revealed that increasing of W(s)/W(g), Q/Q(ms) and gamma significantly increased the adhesion efficiency. Adhesion efficiencies higher than 90% were achieved by selecting precise coating conditions, indicating the feasibility of the process for coating of hard gelatine capsules. (C) 2008 Elsevier B.V. All rights reserved.

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Purpose: The aim of this study was to investigate the impact of acute PaCO(2) temporal variation on the standard base excess (SBE) value in critically ill patients. Methods: A total of 265 patients were prospectively observed; 158 were allocated to the modeling group, and 107 were allocated to the validation group. Two models were developed in the modeling group (one including and one excluding PaCO(2) as a variable determinant of SBE), and both were tested in the validation group. Results: In the modeling group, the mathematical model including SIDai, SIG, L-lactate, albumin, phosphate, and PaCO(2) had a predictive superiority in comparison with the model without PaCO(2) (R(2) = 0.978 and 0.916, respectively). In the validation group, the results were confirmed with significant F change statistics (R(2) change = 0.059, P < .001) between the model with and without PaCO(2). A high correlation (R = 0.99, P < .001) and agreement (bias = -0.25 mEq/L, limits of agreement 95% = -0.72 to 0.22 mEq/L) were found between the model-predicted SBE value and the SBE calculated using the Van Slyke equation. Conclusions: Acute PaCO(2), temporal variation is related to SBE changes in critically ill patients. (C) 2009 Elsevier Inc. All rights reserved.

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A nongravimetric acetyl bromide lignin (ABL) method was evaluated to quantify lignin concentration in a variety of plant materials. The traditional approach to lignin quantification required extraction of lignin with acidic dioxane and its isolation from each plant sample to construct a standard curve via spectrophotometric analysis. Lignin concentration was then measured in pre-extracted plant cell walls. However, this presented a methodological complexity because extraction and isolation procedures are lengthy and tedious, particularly if there are many samples involved. This work was targeted to simplify lignin quantification. Our hypothesis was that any lignin, regardless of its botanical origin, could be used to construct a standard curve for the purpose of determining lignin concentration in a variety of plants. To test our hypothesis, lignins were isolated from a range of diverse plants and, along with three commercial lignins, standard curves were built and compared among them. Slopes and intercepts derived from these standard curves were close enough to allow utilization of a mean extinction coefficient in the regression equation to estimate : lignin concentration in any plant, independent of its botanical origin. Lignin quantification by use of a common regression equation obviates the steps of lignin extraction, isolation, and standard curve construction, which substantially expedites the ABL method. Acetyl bromide lignin method is a fast, convenient analytical procedure that may routinely be used to quantify lignin.

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This study compared an enzyme-linked immunosorbent assay (ELISA) to a liquid chromatography-tandem mass spectrometry (LC/MS/MS) technique for measurement of tacrolimus concentrations in adult kidney and liver transplant recipients, and investigated how assay choice influenced pharmacokinetic parameter estimates and drug dosage decisions. Tacrolimus concentrations measured by both ELISA and LC/MS/MS from 29 kidney (n = 98 samples) and 27 liver (n = 97 samples) transplant recipients were used to evaluate the performance of these methods in the clinical setting. Tacrolimus concentrations measured by the two techniques were compared via regression analysis. Population pharmacokinetic models were developed independently using ELISA and LC/MS/MS data from 76 kidney recipients. Derived kinetic parameters were used to formulate typical dosing regimens for concentration targeting. Dosage recommendations for the two assays were compared. The relation between LC/MS/MS and ELISA measurements was best described by the regression equation ELISA = 1.02 . (LC/MS/MS) + 0.14 in kidney recipients, and ELISA = 1.12 . (LC/MS/MS) - 0.87 in liver recipients. ELISA displayed less accuracy than LC/MS/MS at lower tacrolimus concentrations. Population pharmacokinetic models based on ELISA and LC/MS/MS data were similar with residual random errors of 4.1 ng/mL and 3.7 ng/mL, respectively. Assay choice gave rise to dosage prediction differences ranging from 0% to 30%. ELISA measurements of tacrolimus are not automatically interchangeable with LC/MS/MS values. Assay differences were greatest in adult liver recipients, probably reflecting periods of liver dysfunction and impaired biliary secretion of metabolites. While the majority of data collected in this study suggested assay differences in adult kidney recipients were minimal, findings of ELISA dosage underpredictions of up to 25% in the long term must be investigated further.

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There is a general consensus that in a competitive business environment, firms’ performance will depend on their capacity to innovate. To clarifying how, when and to what extent innovation affects the market and financial performance of firms, the authors deploy seemingly unrelated regression equation model to examine innovation in over 500 Portuguese firms from 1998 to 2004. The results confirm, as theorists have frequently assumed, that innovation positively affects firms’ performance; but they also suggest that the reverse is true, a result that is less intuitively obvious, given the complexity of the innovation process and local, national and global competitive environments.

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There is in the literature a noteworthy lack of sampling methods to be employed in the selection of papaya fruits for genetical improvement purposes. In the present experiment methods applied to melon and watermelon were extended to papaya with good results. The authors tried to correlate the solids content-as determined by a hand refractometer-with total sugars in the fruit. The correlation does exist being significant and positive. A regression equation was worked out; it permits to calculate the total sugar content provided the Brix value determined by the hand refractometer is known. The equation is as follows: a = 0.57 s where a = total amount of sugar as glucose, and s = total solids.

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Searching for a substitute of wheat bran and wheat standard middlings in chick mashes, three experiments were carried out using ground sorghums. In the first one, 30% of Atlas, Kafir e White Afrikan x Sumac (seed chops) were substituted for 30% of wheat by-products. All the rations with sorghum grain gave inferior results. In another experiment, 7, 14, 20 and 30% of sorghum substituted equal percentages of those wheat by-products, the best results having been obtained with 7% of Atlas and 23% of wheat by-products. Finally, in a third experiment, 5% of dried cow manure plus 10, 20 and 30% of ground Atlas sorghum were substituted for 5% of alfalfa hay meal plus, respectively, 10, 20 and 30% of wheat by-products. All results obtained from rations containing sorghum were as good as or better than that given by the ration including alfalfa hay meal and only wheat by-products. Under the conditions of this experiment, 5% of cow manure plus 12,25% of sorghum and 17,75% of wheat by-products is supposed to be the best combination to be recommended, this result having been attained through the study of the regression equation.

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This paper deals with the study by orthogonal polynomials of trends in the mean annual and mean monthly temperatures (in degrees Centigrade) in Campinas (State of São Paulo, Brasil), from 1890 up to 1956. Only 4 months were studied (January, April, July and October) taken as typical of their respective season. For the annual averages both linear and quadratic components were significant, the regression equation being y = 19.95 - 0.0219 x + 0.00057 x², where y is the temperature (in degrees Centigrade) and x is the number of years after 1889. Thus 1890 corresponds to x = 1, 1891, to x = 2, etc. The equation shows a minimum for the year 1908, with a calculated mean y = 19.74. The expected means by the regression equation are given below. Anual temperature means for Campinas (SP, Brasil) calculated by the regression equation Year Annual mean (Degrees Centigrade) 1890 19.93 1900 10.78 1908 19.74 (minimum) 1010 19.75 1920 19.82 1930 20.01 1940 20.32 1950 20.74 1956 21.05 The mean for 67 years was 20.08°C with standard error of the mean 0.08°G. For January the regression equation was y = 23.08 - 0.0661 x + 0.00122 x², with a minimum of 22.19°C for 1916. The average for 67 years was 22.70°C, with standard error 0.12°C. For April no component of regression was significant. The average was 20.42°C, with standard error 0.13°C. For July the regression equation was of first degree, y = 16.01 + 0.0140X. The average for 67 years was 16.49°C, with standard error of the mean 0.14°C. Finally, for October the regression equation was y = 20.55 - 0.0362x + 0.00078x², with a minimum of 20.13°C for 1912. The average was 20.52°C, with standard error of the mean equal to 0.14°C.