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As for detailed analysis, lane blockage probability is interpreted by using a binary logistic regression model.
This analysis, using a binary logistic regression model, was aimed at identifying factors that predicted marrow graft rejection.
Multivariate analysis was also performed using a binary logistic regression models and a forward stepwise (likehood ratio) procedure.
All of the statistical analyses in this study were performed using PASW software, version 18. H1 was tested using a multiple regression analysis and H2, 3, 4, 5, 6, and 7 were tested using a binary logistic regression analysis.
The target of the first probe (into the food-frame or the tool-frame) was examined using a binary logistic regression, with 'subject' and 'food depth' as factors.
We performed multivariate analysis using a binary logistic regression test for risk of BPAR and the Cox regression test for risk of graft loss, respectively (backward stepwise method).
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The first stage uses a binary logistic model to determine the overall crash severity level.
The dependent measure was the dichotomous outcome of hard or easy task choice, and we used a binary logistic distribution to model the probability of choosing the hard-task.
As confirmation, we used a binary logistic regression model.
For multivariate analyses, we used a binary logistic regression.
We used a binary logistic regression model to identify potential predictors for increased anxiety levels.
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