To determine predictors of an ER visit or hospitalization, logistic regression was performed with a forward stepwise method (using a likelihood ratio, with entrance/exit tolerances of 0.05/0.10).
To reduce the number of DXA variables to a significant subset we used a backward stepwise method using the Likelihood ratio test.
Variables for multivariate analysis were selected by the Stepwise Method, using a significance level of <0.010 for entering into or remaining in the model.
A general linear model examined which demographic variables were significant predictors of the degree of change in the performance measure, with a backward stepwise method used to determine the best model.
A backwards-stepwise method using the likelihood ratio was chosen as this avoids suppressive effects, and is recommended when there are no firm hypotheses (Menard, 1995).
All automatic stepwise methods used a probability to remove of 0.5.
To obtain an adequate discriminant function, a stepwise method was used to select the best variables among surface reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll content, leaf water, and lignin-cellulose.
A stepwise method was used to calculate the value of r.
A stepwise method was used to improve the quality of the contigs in the assembly, which was initially defined during the physical map construction of chromosome 3B [ 20] and then modified for the WGP fingerprints [ 32].
In the first regression, a stepwise method was used in which demographic data was entered in step one, trauma history was entered in step two, average FPS to the CS+ was entered in step three, and average SCRs to the CS+ were entered in the final step.
