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In the Cambridge Structural Database of small-molecule structures, more than 95% of the 500,000+ crystals have an R-factor lower than 0.15, and 9.5% have an R-factor lower than 0.03. Crystallographers also use the Free R-Factor ( R F r e e {\displaystyle R_{Free}} ) [ 3 ] to assess possible overmodeling of the data.
An R F value will always be in the range 0 to 1; if the substance moves, it can only move in the direction of the solvent flow, and cannot move faster than the solvent. For example, if particular substance in an unknown mixture travels 2.5 cm and the solvent front travels 5.0 cm, the retardation factor would be 0.50.
The response factor can be expressed on a molar, volume or mass [1] basis. Where the true amount of sample and standard are equal: = where A is the signal (e.g. peak area) and the subscript i indicates the sample and the subscript st indicates the standard. [2]
Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
In contrast to the similar concept called Retention uniformity, R d is sensitive to R f values close to 0 or 1, or close to themselves. If two values are not separated, it is equal to 0. For example, the R f values (0,0.2,0.2,0.3) (two compounds not separated at 0.2 and one at the start ) result in R D equal to 0, but R U equal to 0.3609.
Another function is the multispot response function (MRF) as developed by De Spiegeleer et al.{Analytical Chemistry (1987):59(1),62-64} It is based also of differences product. This function always lies between 0 and 1. When two RF values are equal, it is equal to 0, when all RF values are equal-spread, it is equal to 1.
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.
where δ i is the distance between atom i and either a reference structure or the mean position of the N equivalent atoms. This is often calculated for the backbone heavy atoms C, N, O, and C α or sometimes just the C α atoms.