The fit summary model statistical results obtained from Eq. 1 are shown in Table 3.
In the Fit Summary section, certain data were shown based on the calculations carried out by the software to obtain the best model to predict the most accurate response.
The summary of the fitted distributions for each task are and the result of Goodness of Fit Test given in Table 5.
Summary statistics of the fitted robust linear model was used to identify problematic chips: datasets GSE9844, GSE30784, GSE3524, GSE42743, and GSE2280 contained hybridization artefacts.
To test the robustness of the model we compared the model fit, summary estimates and the exposure response functions of the final models with models that used longer lagged averages (2-day mean of lag 0, 1 combined with 3 day mean of lag 2 5 or lag 0 combined with the mean of lag 1 to 25).
The goodness-of-fit summary of the SEM results indicates that expectations and perceptions are positively correlated, perceptions and loyalty are positively correlated, but expectations and loyalty are not positively correlated.
We then fitted the summary distribution of several Poisson distributions, generated with variable number of different rates, with the observed values.
(d.f.: degree of freedom, AIC: Akaike's Information Criteria, smaller number shows the better fitting) Summary of different fits of age (A), best-fitted period (P), cohort (C), age and period (AP), age and cohort (AC), and age, period and cohort (APC) model of OCC incidence rates C01-C066), from 1986 to 2010.
The SPARTA methodology can be used to examine the fit between summary statistics (attributes) of the input MSA and those statistics computed from the simulated MSAs under the inferred indel parameters.
