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In statistics, pooled variance (also known as combined variance, composite variance, or overall variance, and written ) is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same. The numerical estimate resulting from ...
In statistics and uncertainty analysis, the Welch–Satterthwaite equation is used to calculate an approximation to the effective degrees of freedom of a linear combination of independent sample variances, also known as the pooled degrees of freedom, [1] [2] corresponding to the pooled variance.
Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.
Here, = is the degrees of freedom associated with the i-th variance estimate. The statistic is approximately from the t -distribution since we have an approximation of the chi-square distribution . This approximation is better done when both N 1 {\displaystyle N_{1}} and N 2 {\displaystyle N_{2}} are larger than 5.
Then, a researcher might use sample contrasts between individual sample pairs, or post hoc tests using Dunn's test, which (1) properly employs the same rankings as the Kruskal–Wallis test, and (2) properly employs the pooled variance implied by the null hypothesis of the Kruskal–Wallis test in order to determine which of the sample pairs ...
It naturally breaks down into the part related to the estimation of the mean, and to the part related to the estimation of the variance. The first order condition for maximum, d ln L ( μ , Σ ) = 0 {\displaystyle d\ln {\mathcal {L}}(\mu ,\Sigma )=0} , is satisfied when the terms multiplying d μ {\displaystyle d\mu } and d Σ ...
We could then calculate the sample means within the treated and untreated groups of subjects, and compare these means to each other. In a "paired difference analysis", we would first subtract the pre-treatment value from the post-treatment value for each subject, then compare these differences to zero. See also paired permutation test.
To illustrate, a simple example of this process is to find the mean and variance of the derived quantity z = x 2 where the measured quantity x is Normally distributed with mean μ and variance σ 2. The derived quantity z will have some new PDF, that can (sometimes) be found using the rules of probability calculus. [7]