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Previous studies pointed out the fact that extensive model developments were undertaken between the two series of experiments [e.g., Knutti and Sedlacek, 2013].
All statistical analyses and model developments were conducted using JMP 9.0 at the two-tailed significance level of P < 0.05 (or the 95% confidence level).
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Data sets used for the model development were obtained from simulated model built in CHEMCAD environment.
Variable selection and model development were carried out by stepwise multiple linear regression methodology.
The HTA reports with new model development were further analysed with respect to the applied modelling methods.
The tree species selected for our model development were representative of those found in the highland areas of Ethiopia.
As a separate exercise, all duplicate data from round two not used in model development were scored.
As a preliminary test of the classifier's performance, all data not used in model development were scored, yielding 91.3% sensitivity and 88.5% specificity (Table 4, Figure 1).
The data sources for model development were from the published literature and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) databases [2].
Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear.
All codes used in model development were written using the Python programming language.
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