A normalized function giving the relative amount of a portion of a polymeric
substance with a specific value, or a range of values, of a random variable
Distribution functions may be discrete, i.e. take on only certain specified values
of the random variable(s), or continuous, i.e. take on any intermediate value of the random variable(s), in a given range. Most distributions in polymer science are intrinsically discrete,
but it is often convenient to regard them as continuous or to use distribution functions
that are inherently continuous.
Distribution functions may be integral (or cumulative), i.e. give the proportion of
the population for which a random variable is less than or equal to a given value. Alternatively they may be differential distribution
functions (or probability density functions), i.e. give the (maybe infinitesimal) proportion of the population for
which the random variable(s) is (are) within a (maybe infinitesimal) interval of its (their) range(s).
- Normalization requires that: (i) for a discrete differential distribution function, the sum of
the function values over all possible values of the random variable(s) be unity; (ii) for a continuous differential distribution function, the integral
over the entire range of the random variable(s) be unity; (iii) for an integral (cumulative) distribution function, the function
value at the upper limit of the random variable(s) be unity.
IUPAC. Compendium of Chemical Terminology, 2nd ed. (the "Gold Book"). Compiled by
A. D. McNaught and A. Wilkinson. Blackwell Scientific Publications, Oxford (1997).
XML on-line corrected version: http://goldbook.iupac.org (2006-) created by M. Nic,
J. Jirat, B. Kosata; updates compiled by A. Jenkins. ISBN 0-9678550-9-8. https://doi.org/10.1351/goldbook