Sentence examples for summary of fit from inspiring English sources

Table 5 provides a summary of fit statistics across models for the analysis data set and the validation data set.

A summary of fit statistics and a concentration response curve for one example of each of the major scaffolds identified.

Factorial ANOVA results confirmed good agreement between measured and predicted values, with the summary of fit reported at r = 0.96.

The Summary of Fit section in Table  1 shows that the model accounted for 19.43% of the variation around the mean (R-square).

A summary of fit statistics and a concentration response curve for one example of each of the major scaffolds identified including benzoxazepine, phenylethynyl-phenyl, and benzamide PAMs is detailed in the Supporting Information.

Corresponding loadings for the fifth component reveal that all gene features on the X chromosome are enriched in mono- and di-nucleotide repeats (Additional file 2; a summary of fits for all multivariate models is available in Additional file 3).

The first two data sets have been analysed extensively by Choodari-Oskooei et al. [ 1, 3]- see Additional file 2 for further details on the data sets, prognostic factors included in each study, and the summary of fitted models.

The root mean square percent error (RMSPE) statistic [30] is used for Fig. 4: Open image in new window Fig. 4 Summary goodness-of-fit statistic (RMSPE) Open image in new window (15 where x is the variable of interest, N is the number of observations (years) and superscripts 0 and 1 denote observed and fitted measures respectively.

The incremental model building was driven both by the significance of the estimated parameter coefficients, as well as the summary goodness of fit measures, such as log-likelihood and AIC.

Since the autoregressive models provide superior fit (as indicated by both the summary goodness of fit statistics), as well as satisfy the assumption of independent residuals (as indicated by the graphical diagnostics), it may be concluded that the "ordinary" non-linear models underestimate the standard errors.

The preferred model was a mixture of five components on the basis of summary measures of fit (MAE, RMSE, AIC, BIC) compared to standard linear regression and mixtures with different numbers of components.