Using Central Limit Theorem to Estimate Sample Means in a Stats Class"

AI Thread Summary
The discussion revolves around applying the Central Limit Theorem (CLT) to estimate how many students in a stats class will have sample means below 23.25 after generating random numbers. The class mean is 27 with a standard deviation of 20, and each student generates 64 random numbers. The appropriate formula to use is the standardized statistic, where n equals 64 for each student's sample. Participants confirm that this approach is correct for calculating the expected number of students with sample means below the specified threshold. The conversation emphasizes the importance of correctly applying the CLT in statistical analysis.
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Homework Statement



Each of 180 students in an evening stats and methods class is asked to generate 64 random numbers with a "spinner" that selects numbers from 1 to 50, and then compute the mean of the 64 numbers. The mean for the class as a whole is 27 with a standard deviation of 20. How many of the students would be expected to have their sample means less than 23.25?


Homework Equations





The Attempt at a Solution



Would I use this form of the CLT: \frac{\sqrt{n}(\overline{x} - \mu)}{\sigma}
 
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No one has responded, so I think I'll weigh in - yes, that's the statistic to use, with n = 180.
 
That's what I thought; thanks.
 
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