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95% confidence intervals, given in Table 2, suggest that the groups overlap significantly due to high variance which may indicate regression to the mean in terms of changes in scores.
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Yet, R values were considerably low, a fact that may indicate that regression model may be a poor descriptor of study data, in particular only a minority of measurements was higher than normal.
One of the main issues is the low explanatory power of the OHIP regression, which may indicate the existence of other unobservable factors that affect self-reported measure of oral health.
Variance inflation factor values >2.5 or tolerance <0.4 may indicate concern for multicollinearity in logistic regression models.
Left ventricular size may merely be reflective of the accumulated cardiovascular toxicity of end-stage renal disease and regression of LVM may indicate an attenuation of these effects.
No significant regression models for ST may indicate a lack of pertinent predictor variables measured in this study.
The low PCH scores obtained with the SOM-based measures may indicate that the lower R2 values obtained with the SOM PCH regression models may be caused by the increased genetic 'noise' found in these models.
The use of our approach at other sites may indicate if there is a systematic bias or not in the regressions.
Linear regression analysis as a function of time was performed to check for significant trends which may indicate degradation of the analytes.
This may indicate difficulty concentrating.
These may indicate an imminent volcanic eruption.
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