Usage of "nonlinear regression"

Russian

нелинейная регрессия

Portuguese

regressão não linear

Spanish

regresión no lineal

Catalan

regressió no lineal

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.

non-linear regression

linear regression

нелинейная регрессия

Synonyms

Examples for "non-linear regression"

non-linear regression

Examples for "non-linear regression"

Linear and non-linear regression methods were compared to determine the best fitting of isotherm and kinetic model to experimental data. 
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The relative volume of the stained region was correlated with the number of loading cycles by non-linear regression using a power-law. 
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Data analyses employed ANOVAs and non-linear regressions. 
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The intrinsic kinetic parameters are also determined by comparing predicted values to those of the experimental results by applying non-linear regressions. 
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Usage of nonlinear regression in English

Different exposure indices were estimated using nonlinear regression and Bayesian estimation. 
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The CM Functions and adjustment functions were developed using linear and nonlinear regression models. 
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The nonlinear regression algorithm is based on that of Gauss-Newton. 
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A dedicated nonlinear regression based curve-fitting packages has been developed for quantitative analysis of beta-adrenoceptor subtypes. 
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Pharmacokinetic and pharmacodynamic modeling were performed subject by subject for the 4 doses altogether by nonlinear regression. 
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Here we considered phenomenological and microstructurally motivated constitutive models and identified material parameters for each via nonlinear regression. 
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The binding data were analyzed simultaneously, using a computerized curve-fitting technique with an extended least-squares nonlinear regression program. 
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Focusing on a Markovian version of the model, we develop a novel nonlinear regression model providing nonlinear least square estimators of model parameters. 
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We performed linear and nonlinear regression analyses of the end-systolic points and derived the slope (Ees) and volume at 14 kPa pressure. 
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In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. 
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We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. 
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Elimination constant was calculated based on the dynamics of cisplatin concentration in time period between 1 h to 24 h using nonlinear regression analysis. 
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Nonlinear regression analysis revealed maximum attainable receptor occupancy (E(max)) values close to saturation. 
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The decrease in equivalent wound radius with time was computer-modelled using two linear and three nonlinear regressions. 
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By using both linear and nonlinear regressions, two sets of mathematical models were developed for the prediction of Tg values of sugar caramels. 
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