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?