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The ratio estimator is a statistical estimator for the ratio of means of two random variables. Ratio estimates are biased and corrections must be made when they are used in experimental or survey work. The ratio estimates are asymmetrical and symmetrical tests such as the t test should not be used to generate confidence intervals.
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 ...
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
Raking (also called "raking ratio estimation" or "iterative proportional fitting") is the statistical process of adjusting data sample weights of a contingency table to match desired marginal totals. [1] [2] [3]
In the ratio of Poisson variables R = X/Y there is a problem that Y is zero with finite probability so R is undefined. To counter this, consider the truncated, or censored, ratio R' = X/Y' where zero sample of Y are discounted. Moreover, in many medical-type surveys, there are systematic problems with the reliability of the zero samples of both ...
The way it is done there is that we have two approximately Normal distributions (e.g., p1 and p2, for RR), and we wish to calculate their ratio. [b] However, the ratio of the expectations (means) of the two samples might also be of interest, while requiring more work to develop. The ratio of their means is:
The ratio of the inside-count and the total-sample-count is an estimate of the ratio of the two areas, π / 4 . Multiply the result by 4 to estimate π. In this procedure the domain of inputs is the square that circumscribes the quadrant.
One may wish to compute several values of ^ from several samples, and average them, to calculate an empirical approximation of [^], but this is impossible when there are no "other samples" when the entire set of available observations ,..., was used to calculate ^. In this kind of situation the jackknife resampling technique may be of help.