ssible values for a variable, and the probabilities associated w d with each of those is a probability distribution. distributions we've seen so far, the "variable" of interest here is not an individual ather is a sample statistic. In this case it is the mean of a sample, which is denoted by distribution for a sample statistic is called a "Sampling Distribution". In problem #2 we e the mean of this distribution, so we will be calculating "the mean of the possible sample means 2. NOTE: For this problem, if you wish to use technology, it might be helpful to read the StatCrunch tutorials "STATCRUNCH Tutorial for descriptive statistics(Chapter 3).pdf " and "STATCRUNCH Tutorial for expected value and standard deviation of a random variable(Chapter 5) (b).pdf" For this Sampling Distribution (Frequency Distribution), what is the: (a) Mean (Expected Value) 4 = _ x-P(x) ] (no need to show work if using technology, just mention what you used) NOTE: With more precise notation we might have written: #s = [[* . P( ) | to indicate we taking the mean of the population of all possible sample means Thinking about how the symbol for a sample mean * shows up in the same equation as the symbol for a population mean / may help you think about what a sampling distribution is. When we list all the possible values for something, we are describing a population. So when we list all the possible values for sample means, we have a population of sample means. We are taking the mean of that population, so again we are taking the "mean of the possible sample means". (b) Shape/pattern you would expect, according to the Central Limit Theorem (no need to draw it, just describe it). For this assignment only, ignore the usual minimum sample size requirement for using the Central Limit Theorem, which is normally very important