Sentence examples for sample variance in from inspiring English sources

Some of these variables actually are almost constant, giving thus an estimated zero sample variance in many clusters.

The empirical estimator (i.e., the sample variance in a given ranked set) is most commonly used for estimating the variance in the literature.

The variations in yield and product compositions shown in Table 1 therefore reflect heterogeneity in sample, variance in sample preparation, and product analysis.

Designing an appropriate statistical test requires pre-specification of the size of material difference and knowledge of sample variance in order to calculate the requisite sample size.

A single factor explained 50.5% of sample variance in LD and 51.6% in DD.

Beyond medication use, several additional sources of sample variance in the BD group may have influenced the group differences that were observed.

For each covariate it is defined as the 'standardized' bias defined as the difference of the sample means in the treated and untreated samples as a percentage of the square root of the average of the sample variances in the treated and untreated groups.

7The standardized difference known as the 'standardized bias' is the difference of the sample means in the treated and untreated samples as a percentage of the square root of the average of the sample variances in the treated and untreated groups (Rosenbaum and Rubin, 1985).

6 Mean standardized percent absolute bias is the mean absolute bias of the percent difference of the sample means in the treated and non-treated sub-samples as a percentage of the square root of the average of the sample variances in the treated and non-treated groups [34].

F-Test is performed as shown in the Table 2 where the probability P = 0.50, which is the probability of observing a difference between sample variances in an experiment of the given sample size and used to measure the level of significance at which null hypothesis would be rejected.

To recognize sampling variance in R 2, a recovery is repeated a large number of times, each with a random draw from an anticipated distribution of R 2, to create an empirical Bayes posterior distribution of the recovered statistic.