BUAD 310g Chapter Notes - Chapter 8: Minimum-Variance Unbiased Estimator, Central Limit Theorem, Bias Of An Estimator

• School: USC
• Department: Business Administration
• Course: BUAD 310g
• Professor: Mukherjee Gourab

Document Summary

Some samples could differ greatly from population (particularly if the sample size is. Sampling statistic: random variable whose value depends on which population items happen to be included in the random sample small) Statistical estimation larger samples, sample means would tend to be closer to mean. How to make inferences about a population that take into account four factors: Estimator: statistic derived from a sample to infer the value of a population parameter. Estimate: value of the estimator in a particular sample. Random samples vary so an estimator is a random variable. Sampling error is the difference between an estimate and the corresponding population parameter. Usually the parameter we are estimating is unknown. Sample mean x bar is a random variable that correctly estimates mean on average because sample means that overestimate mean will tend to be offset by those that underestimate mean. Bias: difference between the expected value of the estimator and the true parameter.