 Chapter 9: Sampling Distributions

Key Vocabulary:

§         parameter

§         statistic

§         sampling variability

§         sampling distribution

§         unbiased

§         central limit theorem

§         law of large numbers

Calculator Skills: §         randNorm(m, s, #trials )

9.1    Sampling Distributions (pp.456-469)

1.      Explain the difference between a parameter and a statistic?

2.      Explain the difference between p and ?

3.      What is sampling variability?

4.      What is meant by the sampling distribution of a statistic?

5.      When is a statistic considered unbiased?

6.      How is the size of a sample related to the spread of the sampling distribution?   9.2    Sample Proportions (pp.472-479)

1. In an SRS of size n, what is true about the sampling distribution of when the sample size n increases?

2.      In an SRS of size n, what is the mean of the sampling distribution of ?

3.      In an SRS of size n, what is the standard deviation of the sampling distribution of ?

4.      What happens to the standard deviation of as the sample size n increases?

5.      When does the formula apply to the standard deviation of ?

6.      When the sample size n is large, the sampling distribution of is approximately normal.  What test can you use to determine if the sample is large enough to assume that the sampling distribution is approximately normal?

9.3    Sample Means (pp.481-494)

1. The mean and standard deviation of a population are parameters

What symbols are used to represent these parameters?

2.      The mean and standard deviation of a sample are statistics

What symbols are used to represent these statistics?

3.      Because averages are less variable than individual outcomes, what is true about the standard deviation of the sampling distribution of ?

4.      What is the mean of the sampling distribution of , if is the mean of an SRS of size n drawn from a large population with mean m and standard deviation s

5.      What is the standard deviation of the sampling distribution of , if is the mean of an SRS of size n drawn from a large population with mean m and standard deviation s

6.      To cut the standard deviation of in half, you must take a sample _____ times as large.

7.      When should you use to calculate the standard deviation of ?

8.      What does the central limit theorem say about the shape of the sampling distribution of ?

9.      What is the law of large numbers? 