An IQ test is designed so that the mean is 100 and the standard deviation is 13 for the population of normal adults. Find the sample size necessary to estimate the mean IQ score of statistics students such that it can be said with 90% confidence that the sample mean is within 4 IQ points of the true mean. Assume that o = 13 and determine the required sample size using technology. Then determine if this is a reasonable sample size for a real world calculation. The required sample size is (Round up to the nearest integer.)

Calculate the estimated standard error for the case where the sample standard deviation is 25 and the sample size 137. Answer to three decimal places if necessary, you may round up or down.

On average, a sample of n = 36 scores will provide a better estimate of the population mean than a sample of n = 49 scores from the same population.

The best way to ensure that your sample represents the population it came from accurately, is to make sure you have a sample of at least 10. (n=10) True or false

An IQ test is designed so that the mean is 100 and the standard deviation is 24 for the population of normal adults. Find the sample size necessary to estimate the mean IQ score of statistics students such that it can be said with 90% confidence that the sample mean is within 8 IQ points of the true mean. Assume that o = 24 and determine the required sample size using technology. Then determine if this is a reasonable sample size for a real world calculation. ... The required sample size is. (Round up to the nearest integer.) Would it be reasonable to sample this number of students? O No. This number of IQ test scores is a fairly small number. Yes. This number of IQ test scores is a fairly small number. Yes. This number of IQ test scores is a fairly large number. No. This number of IQ test scores is a fairly large number.

You would like to make an interval estimate of a population mean from a sample of size 25. Assume the population standard deviation is 6. Assuming 98% Confidence, what is the margin for error for the estimate.