Sentence examples for models when fitting from inspiring English sources

Modeling discrimination techniques were used to determine the statistical significance of the different models when fitting experimental cyclic voltammetry data.

Heritabilities and variance components of the phenotypic variance were estimated with restricted maximum likelihood (REML) models, which are preferred over maximum likelihood models when fitting a large number of fixed effects [4].

Table 3 shows the overall summary of the results of filtration models when fitted to the experiments with the 5 kDa UF membrane at all TMPs.

Table 3 shows the coefficients of determination obtained from the filtration models when fitted to the experimental data with the 5 kDa UF membrane.

Table 4 shows the result summary of the filtration models when fitted to the filtration experiments with the 10 kDa UF membrane at the TMPs of 40, 80 and 120 kPa.

Some overlap was evident in weights for the POSG and BG models, when fit to data simulated under a model of exponential growth (POSG).

This analysis had two aims: first, to evaluate the significance of any difference between the two models when fitted to the data; and second, to investigate whether the simulated variation in the PaO2/FiO2 ratio was relevant.

Across simulated models and sample sizes, the parameter estimates for most models, when fit to their simulated datasets, converged by our criterion (i.e., three model fits within 5 log-likelihood units of the best fit) in fewer than 10 iterations.

We informally compared results from both regression models in order to see whether we could use the Poisson model when fitting a model for all types jointly.

The above paragraphs illustrate that, given lack of orthogonality between parameters of a statistical model, when fitting different parameterizations, interpretation of the resulting parameter estimates can be complex.

We are unable to determine the best fitting transmission model when fit using regression assuming Poisson distributed errors.