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Empirical Bayes statistic (EB) was used to estimate whether the variation was systematic; logistic-multilevel modelling determined what proportion of the variation was explained by the hospital; and, shrunken residuals, as provided by multilevel modelling, were plotted to flag hospitals performing worse than expected.
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The stress strain curves of the reconstructed models and the real rock model were plotted in Fig. 7.
The 3D response surface and 2D contours based on the quadratic model were plotted, as shown in Fig. 3.
Assuming random execution of experiments normal probability graphs for the coefficients of a saturated model were plotted to make an initial determination of significant model coefficients.
Therefore four dimensional regression models were plotted against the input variables of temperature, electrical current density and the anode circuit's methanol concentration.
In addition, the residuals of the model were plotted against the observed values, and the relationship between residuals and observed values was analysed with linear regression.
Non-parametric kernel density estimates of the Pearson-type residuals from the two models were plotted.
Predicted means and 95% CIs from the polynomial growth curve models were plotted to graphically illustrate change over time.
The fitted and predicted values of this model were plotted in Figure 2, and the associated environmental variables are shown in Figure 3. Figure 4 showed the time series profile for positive influenza counts in Maricopa County.
The Pearson-type residuals from the adopted final model were plotted against the observed responses and the predicted responses (the latter computed on the scale of the data and incorporating the random effects).
A total of 470 regression models were plotted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com