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The squared deviations are then summed and divided by n − 1 to provide the sample variance.
Characteristics of the sample such as the sample mean, the sample variance, and the sample proportion are called sample statistics.
We provide bounds for tail probabilities of the sample variance.
The sample variance of the sample is defined as (1.2).
In the nearly singular design, the sample variance converges to a singular limit term.
Actually, the sample variance 409.05 of EQ4-4.9 is much larger than the sample mean 30.36.
Only 0.23% of the sample variance could not be satisfactorily explained by the model.
Then, with probability, the sample variance satisfies with given by (1.6).
So the sample variance (widetilde{sigma ^{2}}) is equal to (T_{n,2} v)).
Calculate sample mean and sample variance of the values in throughput vector. 5.
From the mathematical point of view the sample variance is one of the simplest nonlinear statistics.
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