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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.
Under the frequency interpretation of probability, it is assumed that as the length of a series of trials increases without bound, the fraction of experiments in which a given event occurs will approach a fixed value, known as the limiting relative frequency. [7] [8] This interpretation is often contrasted with Bayesian probability.
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 fitness of a genotype is a measure of its reproductive success, often expressed as a fraction of the maximum reproductive success in the population. The formula to calculate the selection coefficient s {\displaystyle s} for a genotype is: s = 1 − W {\displaystyle s=1-W} , where W {\displaystyle W} is the relative fitness of the genotype ...
In mathematics, a rate is the quotient of two quantities, often represented as a fraction. [1] If the divisor (or fraction denominator) in the rate is equal to one expressed as a single unit, and if it is assumed that this quantity can be changed systematically (i.e., is an independent variable), then the dividend (the fraction numerator) of the rate expresses the corresponding rate of change ...
The area of each block is the fraction of the total that each category represents, and the total area of all the bars is equal to 1 (the fraction meaning "all"). The curve displayed is a simple density estimate. This version shows proportions, and is also known as a unit area histogram.
Frequentist probability or frequentism is an interpretation of probability; it defines an event's probability as the limit of its relative frequency in infinitely many trials (the long-run probability). [2] Probabilities can be found (in principle) by a repeatable objective process (and are thus ideally devoid of opinion).
When trying to predict Y, the most naive regression function that we can think of is the constant function predicting the mean of Y, i.e., () = ¯. It follows that the MSE of this function equals the variance of Y; that is, SS err = SS tot, and SS reg = 0.