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Distribution of Sample Mean - Excel quiz

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  • What Excel function is used to find left tail probabilities for sample means in a sampling distribution?
    The =NORM.DIST function is used, with cumulative set to true.
  • What is the first input for the =NORM.DIST function when finding probabilities for sample means?
    The first input is x̄, the sample mean you want to find the probability for.
  • How do you calculate the mean of a sample in Excel?
    Use the =AVERAGE function and select the sample data cells.
  • What value is used as the mean input in =NORM.DIST for sampling distributions?
    The population mean μ is used as the mean input.
  • How do you calculate the standard deviation of the sampling distribution in Excel?
    Divide the population standard deviation σ by the square root of the sample size n.
  • What should the cumulative input be set to in =NORM.DIST when finding left tail probabilities?
    Set cumulative to TRUE to get the cumulative probability.
  • How do you find the probability of getting a sample mean above a certain value in Excel?
    Subtract the left tail probability from one: 1 - NORM.DIST(...).
  • What is the sample size n in the example provided in the transcript?
    The sample size n is 40.
  • What is the population mean μ for the soda bottle example?
    The population mean μ is 16.75 fluid ounces.
  • What is the population standard deviation σ for the soda bottle example?
    The population standard deviation σ is 0.43 fluid ounces.
  • What is the calculated sample mean x̄ for the sample in the example?
    The sample mean x̄ is 16.755.
  • What is the calculated standard deviation of the sampling distribution in the example?
    It is approximately 0.01.
  • What is the probability of collecting a second sample with a lower sample mean than 16.755?
    The probability is about 0.68, or 68%.
  • What is the probability of collecting a second sample with a higher sample mean than 16.755?
    The probability is about 0.32, or 32%.
  • What theorem allows us to use the normal distribution for sample means when n is large?
    The Central Limit Theorem allows this, as sampling distributions become normal when n ≥ 30.