The Bland and Altman method showed no statistical significant difference between the two spectrometric methods for 95% confidence interval.
Our analysis based on Monte Carlo statistical method shows that the success of image segmentation depends on object-background intensity difference, object size and noise measurement, however is unaffected by location of the object on that image.
Generally, the performance of almost all the models is more accurate for storm E than for F. The least squares method shows satisfactory results, considering its statistical measure values for both events.
Regarding Dr. Ideker's comments on comparisons to other methods, we feel that showing statistical significance and biological interpretations is sufficient for a publication on this topic.
The method shows better accuracy and precision and was validated through a variety of statistical parameters.
The MM method showed the highest statistical precision suggesting it is the most appropriate method to use for analysing the FREE trial data.
In the present analysis of the FREE data, the MM method showed the highest statistical precision without the unsubstantiated assumptions required for LOCF.
In fact, the method showed through mathematical and statistical principles the relationships in science early studied by Merton (1942 [1979]).
A boxplot statistical method showed that largest numbers of anomalies are present in all three samples from Lukina jama located on Northern Velebit Mountain, where a whole series of elements, including many heavy metals, show extreme values (Pb, Cu, Zn, Mn, Ni, Cr, Co, Ba, K, Mg, Li, Be, Al, U, Si, Ti, W, Fe, As).
The differences showed statistical significance.
