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The cumulative frequency is the total of the absolute frequencies of all events at or below a certain point in an ordered list of events. [1]: 17–19 The relative frequency (or empirical probability) of an event is the absolute frequency normalized by the total number of events:
Allele frequency, or gene frequency, is the relative frequency of an allele (variant of a gene) at a particular locus in a population, expressed as a fraction or percentage. [1] Specifically, it is the fraction of all chromosomes in the population that carry that allele over the total population or sample size.
In probability theory and statistics, the empirical probability, relative frequency, or experimental probability of an event is the ratio of the number of outcomes in which a specified event occurs to the total number of trials, [1] i.e. by means not of a theoretical sample space but of an actual experiment.
Frequency distribution: a table that displays the frequency of various outcomes in a sample. Relative frequency distribution: a frequency distribution where each value has been divided (normalized) by a number of outcomes in a sample (i.e. sample size). Categorical distribution: for discrete random variables with a finite set of values.
Relative species abundance distributions are usually graphed as frequency histograms ("Preston plots"; Figure 2) [7] or rank-abundance diagrams ("Whittaker Plots"; Figure 3). [8] Frequency histogram (Preston plot): x-axis: logarithm of abundance bins (historically log 2 as a rough approximation to the natural logarithm)
In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution.
A formula for calculating the variance of an entire population of size N is: ... represent the frequency and the relative frequency at bin and = = is the ...
This theorem makes rigorous the intuitive notion of probability as the expected long-run relative frequency of an event's occurrence. It is a special case of any of several more general laws of large numbers in probability theory. Chebyshev's inequality. Let X be a random variable with finite expected value μ and finite non-zero variance σ 2.