Firstly, the analyser was tested for calibration and its analytical performance for BTEX monitoring compared with a more classical analytical method, namely gas chromatography coupled to a flame ionization detector (GC-FID).
The set of points (combinations tested) for calibration of the models was selected using the Dohelert network, which is a design optimization strategy that allows the effective estimation of the models coefficients.
This logistic regression model was tested for calibration with a Hosmer-Lemeshow Χ2 and the possible multicollinearity of the model was checked with an analysis of tolerance and inflation statistics.
This data mining approach was tested for the calibration of a CA model to simulate land-use changes in a portion of the Elbow River watershed adjacent to the City of Calgary, in southern Alberta, Canada.
The instrument was tested for XY calibration using a silicon substrate grid containing an array of silicon oxide squares of height 100 nm separated by 10 μm.
The performance of the models was evaluated using the C-Statistics for discrimination and the Hoshmer-Lemeshow test for calibration.
To overcome the limitations of the H-L, we combined the classic H-L test with a new statistical test for calibration, the GiViTI calibration belt [ 30, 31].
The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration.
Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models.
Third, we assessed the model performance of the modified CURB-65 score, which was made by incorporating CRP information into the CURB-65 model, using a calibration plot and Hosmer-Lemeshow test for calibration and AUC for discrimination.
