The Sample mean is a minimum-variance unbiased point estimate of the mean of a normally distributed population
Further Explanation:
Sample mean is the mean of sample data.
The sample parameter is an unbiased point estimated of the mean of a normally distributed population.
The sample parameter is sample mean.
An estimator is said to be an unbiased estimator if,
An unbiased estimator is when the mean of the statistic’s sampling distribution is equal to the population’s parameter. This means the same estimate. If the statistic equals the parameter, then it’s unbiased estimator.
The biased estimator is the parameter when the sample parameter is not equal to the population parameter. This mean the sample mean is not equal to population mean.
Therefore, the Sample mean is a minimum-variance unbiased point estimate of the mean of a normally distributed population.
Learn more:
1. Learn more about normal distribution brainly.com/question/12698949
2. Learn more about standard normal distribution brainly.com/question/13006989
3. Learn more about confidence interval of mean brainly.com/question/12986589
Answer details:
Grade: College
Subject: Statistics
Chapter: Normal distribution
Keywords: standard normal distribution, standard deviation, test, measure, probability, low score, mean, repeating, indicated, normal distribution, percentile, percentage, undesirable behavior, proportion, unbiased estimator, unbiased, biased, minimum variance, normally distributed population.