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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 ...
where ^ = are the group event rates and ~ = + + is the pooled event rate. The power of this test is similar to that of Boschloo's test in most scenarios. In some cases, the Z {\displaystyle Z} -Pooled test has greater power, with differences mostly ranging from 1 to 5 percentage points.
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.
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.
The following 57 pages are in this category, out of 57 total. ... Pooled variance; Principle of marginality; R. Random effects model; Repeated measures design;
Pooled OLS [clarification needed] can be used to derive unbiased and consistent estimates of parameters even when time constant attributes are present, but random effects will be more efficient. Random effects model is a feasible generalised least squares technique which is asymptotically more efficient than Pooled OLS when time constant ...
The term "Z-test" is often used to refer specifically to the one-sample location test comparing the mean of a set of measurements to a given constant when the sample variance is known. For example, if the observed data X 1 , ..., X n are (i) independent, (ii) have a common mean μ, and (iii) have a common variance σ 2 , then the sample average ...
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.