The distributive normalization requires that the priorities be divided by the square root of the sum of each squared element in a column as shown in the equation below and Table 11: r_{ia} = frac{{x_{ia} }}{{sqrt {mathop sum nolimits_{a = 1}^{n} x_{ia}^{2} } }} quad, {text{for}};a = 1,; ldots, n quad {text{and}}quad i = 1,; ldots, ;m.
It shows the sum of squares and mean square of each factor, F value as well as Prob > F values.
CS measurement was measured monocularly in the trial, therefore binocular CS transition rates were estimated using a published algorithm, which estimates binocular CS to be the square root of the sum of the square of each eye [ 16].
Define D i) = || gene(Gh, i) – gene(Gb, i) || for i = 1,..., 12,063, where || A ||2 = sum of square of each entry in vector A. Sorting D values in a descending order gives a ranking for all of the genes in which top-ranked genes are more discriminating than lower ranked genes.
Spectra were therefore pre-processed using the following routine: (i) baseline correction was performed using AsLS on the raw MS profiles; (ii) this was followed by spectral alignment using icoshift (Fig. 1) and (iii) finally, following this step these spectra were scaled so that the sum of square of each spectrum equals to 1.
The percentage variation explained by each source of variation was calculated for the sum of squares of each effect by total sum of squares, according to the report of (Pillai et al.2010).2010
The equivalent channel model of a multipath fading channel using a MRC Rake receiver and flat fading has been approximated by a fading channel with fading amplitude is a square root of a sum of a square amplitude of each fading[18, 19].
sum of square Adj.sum of square Adj.
where Sr2 and Se2are mean of the model and residuals square respectively, obtained by dividing the sum of squares of each of the two sources of variation, the model, and the error variance, by the respective degrees of freedom (DF) [27].
The value of the mean squares is calculated by dividing the sum of squares of each variable by their respective degree of freedom.
