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Considering the actual needs, the CFA results often require overall model fitting to evaluate the fitting degree between model and data.
We use peak and lattice fitting to evaluate the precision of the corrected exit waves.
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Mixed linear models were fitted to evaluate the extent to which duration spent in each LULC category can explain individuals' time in MVPA before school, after school, and during the weekends.
The lack of fit values (1.22 for PPAP and 3.68 for ATOP) were not significant with respect to their corresponding pure error, which proved that both models could be fitted to evaluate the responses.
To analyze increased training response, a general linear model was fitted to evaluate the effects of marker (wild-type versus Myog-deleted groups), training status, and their interaction on dependent variable distance.
Cox proportional hazards models were fitted to evaluate the cumulative survival probabilities among the BMI groups.
The concentration response relationships were fitted to evaluate the half-maximum inhibitory concentration (Fig. 2C).
Similar mixed models will be fit to evaluate the effect of adjusting for covariates.
Regression models were fitted to evaluate the pain-depression-QoL relationships.
An initial univariate logistic regression model was fitted to evaluate the prognostic value of each variable.
Univariate Cox proportional hazards models were fitted to evaluate the effects of continuous variables on time-to-event outcomes.
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