The Practice of Statistics



Chapter 18: Sampling Distribution Models

Key Vocabulary:

|sampling distribution model |sampling variability |Central Limit Theorem |

|sampling error |sampling distribution model for a proportion |sampling distribution model for a mean |

Calculator Skills:

|normalcdf( | | |

1. What is meant by the sampling distribution model of a sample proportion?

2. What is meant by sampling variability?

3. What is the difference between [pic] and [pic]?

4. What is the “catch” when using the normal model to approximate the distribution of sample proportions?

5. Describe the conditions for using the Normal model for the distribution of sample proportions.

6. What is the sampling distribution model for a proportion?

7. Why are sampling distribution models so important to Statistics?

8. State the Central Limit Theorem (CLT).

9. State the assumptions and conditions associated with the CLT.

10. What is the sampling distribution model for a mean?

11. _____ vary less than individual data values.

12. In order to decrease the standard deviation of the sampling distribution by half, what should be done to the sample size?

13. The Central Limit Theorem doesn’t talk about the distribution of data from the sample. It talks about the sample means and sample proportions of _____ _____ _____ _____ drawn from the same population.

14. Explain the statement, “at the heart is the idea that the statistic itself is a random variable”.

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