population variance वाक्य
उदाहरण वाक्य
मोबाइल
- The population variance matches the variance of the generating probability distribution.
- The following estimate only replaces the population variances by the sample variances:
- To do this, two estimates are made of the population variance.
- Most intra-population variance of height is genetic.
- There is " no " assumption that the underlying population variances are equal.
- For small samples, it is customary to use an unbiased estimator for the population variance.
- A well-known example arises in the estimation of the population variance by sample variance.
- This is a closely related identity that is structured to create an unbiased estimate of the population variance
- I say primitive because the population variance is an expectation, while the population SD is not.
- Secondly, the sample variance does not generally minimize mean squared error between sample variance and population variance.
- So for example an unbiased estimate of the population variance ( the second central moment ) is given by
- For such small samples, a test of equality between the two population variances would not be very powerful.
- Conversely, the sample variance is an unbiased estimate of the population variance, but is not Fisher consistent.
- Mo et al . refined the idea by noting that the population variance of the feature should be taken into account.
- The computational formula for the population variance follows in a straightforward manner from the linearity of expected values and the definition of variance:
- The true distribution of the test statistic actually depends ( slightly ) on the two unknown population variances ( see Behrens Fisher problem ).
- Under the assumption of equal population variances, the pooled sample variance provides a higher precision estimate of variance than the individual sample variances.
- However, in the Behrens Fisher problem, the two population variances are not known to be equal, nor is their ratio known.
- Estimating the population variance by taking the sample's variance is close to optimal in general, but can be improved in two ways.
- The former can give an unbiased s _ p ^ 2 to estimate \ sigma ^ 2 when the two groups share an equal population variance.
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