Results of comparisons of intercepts on these data were presented in a prior publication (Dodds and Oakes 2004).
Linearity of the calibration was confirmed using the correlation coefficient and comparison of the intercept with zero.
Comparison of slopes and intercepts of regression lines were performed by unpaired t-test (41).
A comparison of slopes and intercepts between regions was made using dummy variables in the REG procedure, then comparing AIC between different models [ 43].
The hierarchical Bayesian approach allowed for the comparison of uncertainty in intercept and slope model parameters and the optimization of experiment design.
Covariance analysis and comparison of the slopes and intercepts were used to compare the regression models from the two groups.
In addition, with 66 participants in the study, a test working at the 0.05 level would be expected to find approximately 3 (0.05 x 66) 'outliers.' Covariance analysis and comparison of the slopes and intercepts were used to compare the regression models between the two groups.
†p value for comparison of respectively age and intercept coefficients with the "null" distribution of major epidemic seasons.
For all models, the likelihood ratio test was used for comparison of nested models (random intercept and random coefficients models).
Comparison of regression equations (slopes and intercepts) are made for some vehicle pairs (car car, car auto, car bike and auto bike) with significant data for both the conditions.
