How large does n have to be for Z and t intervals?

被引:37
作者
Boos, DD [1 ]
Hughes-Oliver, JM [1 ]
机构
[1] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
关键词
central limit theorem; confidence interval; convergence to normality; Edgeworth expansion; kurtosis; skewness; t statistic;
D O I
10.2307/2686030
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Students invariably ask the question "How large does n have to be fur Z and t intervals to give appropriate coverage probabilities?" In this article we review the role of root beta(1)(X)/root beta, where root beta(1)(X) is the skewness coefficient of the random sample, in the answer to this question. We also comment on the opposite effect that root beta(1)(X) has on the behavior of t intervals compared to Z intervals, and we suggest simple exercises for deriving rules of thumb for n that result in appropriate confidence interval coverage. Our presentation follows the format of lesson plans for three course levels: introductory, intermediate, and advanced. These lesson plans are sequentially developed, meaning that the lesson plan for an intermediate level course includes all activities from the lesson plan for an introductory course, but with additional explanations and/or activities.
引用
收藏
页码:121 / 128
页数:8
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