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However, these formulas are not a hard rule and the resulting number of classes determined by formula may not always be exactly suitable with the data being dealt with. Calculate the range of the data (Range = Max – Min) by finding the minimum and maximum data values. Range will be used to determine the class interval or class width.
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.
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
Suppose that for some given data we have two statistical models, M 1 and M 2. Also suppose that AIC(M 1) ≤ AIC(M 2). Then the relative likelihood of M 2 with respect to M 1 is defined as follows. [8] ( ())
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.
If ¯ is the average frequency of an allele in the total population, is the variance in the frequency of the allele among different subpopulations, weighted by the sizes of the subpopulations, and is the variance of the allelic state in the total population, F ST is defined as [2]
The relative efficiency of two unbiased estimators is defined as [12] (,) = [()] [()] = ()Although is in general a function of , in many cases the dependence drops out; if this is so, being greater than one would indicate that is preferable, regardless of the true value of .
In probability theory and statistics, the index of dispersion, [1] dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized measure of the dispersion of a probability distribution: it is a measure used to quantify whether a set of observed occurrences are clustered or dispersed compared to a standard ...