When looking at gender as a predictor of UREs, data analysis indicates that gender was not a predictor of any of the response variables in this study.
When looking at first-generation status as a predictor of UREs, data analysis shows no statistical significance.
In this chapter we present a layered architecture infrastructure, based on two Decision Tree predictor hardware implementations, suitable for medical data analysis in real-time and aimed at dealing with a wide data volume and preserving a good hardware resources efficiency.
Data analysis used single and multiple predictor variable logistic regression.
It is shown that the application of functional reconstruction together with a preprocessing of the time series by means of chaotic data analysis methods furnishes an efficient on-line nonlinear predictor.
One approach to functional data analysis uses generalized additive models to estimate functions of predictor variables, adding together each effect to predict the response variable.
Descriptive data analysis was done to generate frequencies and trends across predictor variables.
Based on the extent and nature of missing data (outcomes, predictors, baseline, and items from scales), we will use one of several strategies for data analysis.
Data analysis was done using SPSS v. 19 and multiple logistic regressions were used to identify predictors of CAM use.
Data analysis was done by Greg Whyte.
