Sentence examples for variable the sum of from inspiring English sources

In One way ANOVA, one independent variable is selected and with respect to this independent variable the Sum of Squares (SS), Mean Square (MS), Degrees of Freedom (DF), F-ratio, and p-level are determined (Howladar and Rahman 2016).

20 The variable is a continuous variable, the sum of turn-constructional units, and can vary between 0 and ∞.

For the Total DL variable, the sum of the 19 indicator variables that ranged in value from 0 to 3 could result in a score ranging from 0 to 57.

A pattern of use, i.e. the outcome variable, was converted into a categorical variable: the sum of three variables was obtained (pill used for birth control purposes, doctor consultation obtained, and pill was prescribed).

In [6] Pečarić showed that instead of variables the sum of which is equal to 2a, we can use variables the difference of which is constant, and that result becomes a source of some further generalizations [[2], pp.74, 75].

For a segment of all variables, the sum of the fitting error on all variables of multivariate time series is as follow: ( {mathrm{e}}_{total} = {displaystyle sum_{i=1}^m{e}_i}, ) where e total denotes a total fitting error of all the variables in a segment.

Additionally, we created three other variables: the sum of the individually calculated molar mass of all PAH metabolites (ΣmolPAHs), the sum of the naphthalene metabolites (ΣNAPHT), and the sum of the metabolites of the PAHs with three (fluorene and phenanthrene) or four (pyrene) benzene rings (Σmol3 4PAHs).

In the table the three univariate regressions for each one of the predictor variables are shown first, followed by a model where the independent variable is the sum of 'positive' predictor variables.

23 As with the victimisation variable, we created the bullying variable from the sum of all questions relating to perpetration of bullying.

Analogously, conditioning a sCMP variable on the sum of two independent sCMP variables produces a generalized form of the CMB distribution; we denote this as the gCMB distribution.

The Markov models ( MM1 or MM2) can only be solved with simple cokriging; using ordinary cokriging would lead to ignoring the secondary variable since the sum of weights for the secondary variable must be equal to zero.