A good estimator always leads to a good estimate.
Answers
Answer:
good line , what was the question
Explanation:
One area of concern in inferential statistics is the estimation of the population parameter from the sample statistic. It is important to realize the order here. The sample statistic is calculated from the sample data and the population parameter is inferred (or estimated) from this sample statistic. Let me say that again: Statistics are calculated, parameters are estimated.
We talked about problems of obtaining the value of the parameter earlier in the course when we talked about sampling techniques.
Another area of inferential statistics is sample size determination. That is, how large of a sample should be taken to make an accurate estimation. In these cases, the statistics can't be used since the sample hasn't been taken yet.
Point Estimates
There are two types of estimates we will find: Point Estimates and Interval Estimates. The point estimate is the single best value.
A good estimator must satisfy three conditions:
Unbiased: The expected value of the estimator must be equal to the mean of the parameter
Consistent: The value of the estimator approaches the value of the parameter as the sample size increases
Relatively Efficient: The estimator has the smallest variance of all estimators which could be used