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The pooled variance is an estimate of the fixed common variance underlying various populations that have different means. We are given a set of sample variances s i 2 {\displaystyle s_{i}^{2}} , where the populations are indexed i = 1 , … , m {\displaystyle i=1,\ldots ,m} ,
A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews .
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.
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.
The power of this test is similar to that of Boschloo's test in most scenarios. In some cases, the -Pooled test has greater power, with differences mostly ranging from 1 to 5 percentage points. In very few cases, the difference goes up to 9 percentage points. This test can also be modified by the Berger & Boos procedure.
Total variation distance is half the absolute area between the two curves: Half the shaded area above. In probability theory , the total variation distance is a statistical distance between probability distributions , and is sometimes called the statistical distance , statistical difference or variational distance .
The following 57 pages are in this category, out of 57 total. ... Pooled variance; Principle of marginality; R. Random effects model; Repeated measures design;
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.