sample estimates造句

• Quenouille invented this method with the intention of reducing the bias of the sample estimate.
• The sample estimates of the mean vector and variance-covariance matrices will substitute the population quantities in this formula.
• Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population.
• After then, many other algorithms have been proposed, based on such concepts as discrete morse theory and finite sample estimating.
• The sample estimate was 71, 866.333 baptisms per year over this period giving a ratio of one registered baptism for every 28.35 persons.
• Often, if just a little bias is permitted, then an estimator can be found with lower MSE and / or fewer outlier sample estimates.
• Bootstrapping allows assigning measures of accuracy ( defined in terms of bias, variance, confidence intervals, prediction error or some other such measure ) to sample estimates.
• Under plausible conditions, 68 percent of sample estimates would lie within one standard error of the value that would be obtained from a complete count of the population.
• Some others of the standard estimates associated with the normal distribution are also inadmissible : for example, the sample estimate of the variance when the population mean and variance are unknown.
• Cluster sampling ( also known as clustered sampling ) generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between one another as compared to the within-cluster variation.
• Although the values of the theoretical covariances and correlations are linked in the above way, the probability distributions of sample estimates of these quantities are not linked in any simple way and they generally need to be treated separately.
• A 3 % margin of error means that if the same procedure is used a large number of times, 95 % of the time the true population average will be within the 95 % confidence interval of the sample estimate plus or minus 3 %.
• This turns out to be a general feature of empirical Bayes; the point estimates for the prior ( i . e . mean ) will look like a weighted averages of the sample estimate and the prior estimate ( likewise for estimates of the variance ).
• In the model, the error covariance is stated to be a diagonal matrix and so the above minimization problem will in fact yield a " best fit " to the model : It will yield a sample estimate of the error covariance which has its off-diagonal components minimized in the mean square sense.
• Interval measurements can also be used to generalize distributional estimates based on the method of matching moments or maximum likelihood, that make shape assumptions such as normality or lognormality, etc . Although the measurement uncertainty can be treated rigorously, the resulting distributional p-box generally will not be rigorous when it is a sample estimate based on only a subsample of the possible values.