nonlinear regression


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nonlinear regression

[′nän‚lin·ē·ər ri′gresh·ən]
(statistics)
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publishes scientific software, including: GraphPad Prism combines 2D scientific graphing, biostatistics with explanations, and curve fitting via nonlinear regression.
Table 2 shows the outcomes of nonlinear regression analysis applied to the thirteen drying models to the experimental data for non-pretreated-half, non-pretreated-quarter, pretreated-half and pretreated-quarter samples with [R.sup.2], SEE, and RSS.
These growth curves are mainly fitted using nonlinear regression. The classical method of maximum likelihood is mostly employed that treats the parameters of growth models fixed constants and estimates the unknown parameters using iterative algorithms.
(2018a), used the AIC selection criterion to compare nonlinear regression models to describe the growth of cocoa and Asian pear fruits, respectively, and observed that lower AIC values indicated the nonlinear regression model as the most adequate to describe data.
Fitting Models to Biological Data Using Linear and Nonlinear Regression: A practical guide to curve fitting.
Terza, Basu, and Rathouz (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts.
The model needs to be tuned using nonlinear regression analysis in order to estimate the model parameters instead of traditional methods with parameters defined at rating conditions.
For example, a nonlinear regression model can write the increase of the number of thrips found in a plant, after the seedlings emergence, related to time.
Moreover, PLS can be extended to nonlinear regression and compared with SVR with the same kernel.
Multifactorial nonlinear regression analysis was performed to define the model that would mathematically express the general physical condition, HRQOL, and the emotional state of COPD patients on the basis of pulmonary function parameters, smoking habits, and exacerbation frequency.
Multiple linear regression (MLR), partial least squares (PLS), multiple nonlinear regression (MNLR), and cross-validation analyses were applied to a series of pyrazol inhibitors in order to develop a QSAR model to reliably predict anticancer activity.

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