MATH 477 Study Guide - Final Guide: Statistical Parameter, Random Variable, If And Only If

• School: ACU
• Department: Mathematics/Mathematics Workshop
• Course: MATH 477
• Professor: Gregory Chitwood

Document Summary

An estimator is said to be a sufficient estimator of if has all information relevant to parameter . An estimator is said to be a sufficient, if the statistics used an estimator uses all the information i. e. contain in the sample. Any statistics i. e. not computed from all values in the sample is not a sufficient estimator. The sample mean x is a sufficient estimator of . This implies that x contains all the information in the sample relative to the estimation of the population parameter. And no other estimator such as the sample median, mode etc. calculated from same sample can add any information concerning . The sample proportion p is also a sufficient estimator of the population proportion. If depends on the unknown values of the parameter then is said to be the sufficient estimator of iff be a random sample of a random variable x, whose distribution.