goodness of fit

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goodness of fit

[¦gu̇d·nəs əv ′fit]
(statistics)
The degree to which the observed frequencies of occurrence of events in an experiment correspond to the probabilities in a model of the experiment. Also known as best fit.

goodness of fit

the degree to which observations in a data set conform to an expected distribution predicted from a model. See also CHI SQUARE2 ).
References in periodicals archive ?
Descriptive statistics, including mean, median, standard deviation, and coefficient of variation, and exploratory distributional plots (box-and-whisker plots and nonnal probability plots) were calculated on the individual samples for each treatment group, and probability values (P values) of univariate goodness-of-fit tests for normality were evaluated (Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smimov; Table 1).
The analysis of covariance structures: Goodness-of-fit indices, Sociological Methods and Research 11: 325-344.
However, as a result of the sensitivity of the traditional chi-square test as it relates to sample size, researchers (Browne & Cudeck, 1993; Steiger & Lind, 1980; Steiger, 1989) suggest using the RMSEA as the principal goodness-of-fit index.
Goodness-of-fit test for a logistic regression model fitted using survey sample data.
Akaike's information criterion (AIC) was used to rank and select the model that achieved an optimal balance between the parsimony of the model and the goodness-of-fit, where parsimony decreases as the number of parameters in the model increases.
The fact that electric utility rate structures have an important demand component along with energy-use rates suggests which pertinent statistical goodness-of-fit measures to consider.
Table 6 summarizes the results of the goodness-of-fit tests for Block 1.
The normed chi-square ([chi square]) goodness-of-fit test ([chi square]/df), root mean square error of approximation (RMSEA), and various incremental (normed fit index [NFI] and comparative fit index [CFI]), predictive (expected cross-validation index [ECVI], and Akaike information criteria [AIC]) and absolute (goodness-of-fit index [GFI]) fit indices assessed the quality of the model fit to the data (Clayton and Pett 2011).
At present, however, this major methodological advance can founder due to the lack of reliable goodness-of-fit tests of the initial model.
With 364 valid samples, 26 observed variables, six endogenous latent variables (n), and one exogenous latent variable ([xi]) (Table 6), the goodness-of-fit for the theoretical SEM model was examined stepwise.