Search results
Results From The WOW.Com Content Network
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Donate; Pages for logged out editors learn more
The coefficient of variation (CV) is defined as the ratio of the standard deviation to the mean , [1] It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningful zero ( ratio scale) and hence allow relative comparison of two ...
Stoichiometry. A stoichiometric diagram of the combustion reaction of methane. Stoichiometry ( / ˌstɔɪkiˈɒmɪtri /) is the relationship between the weights of reactants and products before, during, and following chemical reactions . Stoichiometry is founded on the law of conservation of mass where the total mass of the reactants equals the ...
In chemistry, the mole fraction or molar fraction, also called mole proportion or molar proportion, is a quantity defined as the ratio between the amount of a constituent substance, ni (expressed in unit of moles, symbol mol), and the total amount of all constituents in a mixture, ntot (also expressed in moles): [1] It is denoted xi (lowercase ...
In step-growth polymerization, the Carothers equation (or Carothers' equation) gives the degree of polymerization, Xn, for a given fractional monomer conversion, p . There are several versions of this equation, proposed by Wallace Carothers, who invented nylon in 1935.
Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero. See also
Confusingly, sometimes when people refer to wMAPE they are talking about a different model in which the numerator and denominator of the wMAPE formula above are weighted again by another set of custom weights .
One can then also calculate the mean square of the model by dividing the sum of squares of the model minus the degrees of freedom, which is just the number of parameters. Then the F value can be calculated by dividing the mean square of the model by the mean square of the error, and we can then determine significance (which is why you want the ...