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A Note on Pooling Variances
关于方差合并估计的一个说明
The estimation of variance in the normal distribution is studied under statistical inference based on conditional specification. When there are two samples available for estimating the variance and it is not certain whether the two samples are from the same population, the experimenter usually uses a test to resolve the uncertainty. When the test is not significant, the samples are pooled to obtain a pooled estimator; otherwise, the individual sample variance is used. The…
A Note on Pooling Variances
关于方差合并估计的一个注记
The estimation of variance in the normal distribution is studied under statistical inference based on conditional specification. When there are two samples available for estimating the variance and it is not certain whether the two samples are from the same population, the experimenter usually uses a test to resolve the uncertainty. When the test is not significant, the samples are pooled to obtain a pooled estimator; otherwise, the individual sample variance is used. The…
Power of Analysis of Variance Test Procedures for Incompletely Specified Fixed Models
不完全指定固定模型中方差分析检验的水平与功效
Derivations are given of the size and power of a test of a hypothesis for an incompletely specified fixed linear model. Such a testing procedure involves a test of a hypothesis of main interest subsequent to a preliminary test to determine the inclusion or not of a term in the tentatively specified complete fixed linear model. An evaluation of the formulas of the size and power was made over a wide range of values of the parameters involved, and recommendations concerning…
Inferences Concerning a Population Correlation Coefficient from One or Possibly Two Samples Subsequent to a Preliminary Test of Significance
预先显著性检验后基于一个或两个样本的总体相关系数推断
Summary It is shown in this paper that under certain restrictions on population parameters, estimates formulated on the basis of a preliminary test of significance concerning inferences for a population correlation coefficient are better than the usual unbiased estimators.