Asked by Allie Swindell on Sep 29, 2024
Verified
An unbiased estimator of a population parameter is defined as:
A) an estimator whose expected value is equal to the parameter.
B) an estimator whose variance is equal to one.
C) an estimator whose expected value is equal to zero.
D) an estimator whose variance goes to zero as the sample size goes to infinity.
Population Parameter
A numerical value that represents a characteristic of a population, such as the population mean or variance.
Expected Value
The mean of all possible values for a random variable, weighted by their respective probabilities.
Variance
A measure of the dispersion or spread of a set of data points, calculated as the average of the squared differences from the mean.
- Apprehend the differences and similarities of unbiased and biased estimators.
- Delineate the characteristics of consistency and unbiasedness in estimators, making clear the distinction between the two.
Verified Answer
MY
Mehmet Y?ld?r?mabout 7 hours ago
Final Answer :
A
Explanation :
An unbiased estimator is one whose expected value is equal to the parameter being estimated. This means that, on average, the estimator will produce an estimate that is exactly equal to the true value of the parameter. This is the most desirable property for an estimator to have.
Learning Objectives
- Apprehend the differences and similarities of unbiased and biased estimators.
- Delineate the characteristics of consistency and unbiasedness in estimators, making clear the distinction between the two.