Monte Carlo evaluations ofgoodness of fit indices for structural equation models.

While there are variousGoodness of Fit Indexes, in application only 4 - 5 of them are in widespread use (Cengiz and Kirkbir, 2007).

Table-I illustrates, according to the literature, the lowest and highest values of the scales related to some goodness of fit14 and thegoodness of fit indexes obtained from this study.

The systematic uncertainties will also influence thegoodness of fit. The systematic uncertainties include the uncertainty of the center-of-mass energy determination, parametrization of the BW function, the cross section measurement, and the uncertainty of [psi](4160)'s mass and width.

Table for estimating thegoodness of fit of empirical distributions.

In the statistical comparison of the data mining algorithms in animal science,goodness of fit criteria have been highlighted very poorly with the exception of few researches (Grzesiak and Zaborski, 2012; Ali et al., 2015).

The performance of the different models was compared using differentgoodness of fit statistics.

To examine thegoodness of fit, different authors suggest indexes that reveal whether the sample data supports the theory set out, such as the chi-squared statistic ([chi square]), the GFI (goodness of fit index), the RMSEA (root mean square error of approximation), the SRMR (square root mean residual), or the AGFI (adjustedgoodness of fit index).

The use of the chi-square test forgoodness of fit has three assumptions: (i) observations are independent, (ii) categories are mutually exclusive, and (iii) categories are exhaustive [2].

Another approach for testinggoodness of fit is Chi-square test which can be defined as: