Chapter 18

Question 1

Sampling distribution model

Answer 1

Different random samples gibe different values for a statistic. The sampling distribution model shows the behavior of the statistic over all the possible samples for the same size n. ;)

Question 2

Sampling Variability Sampling Error

Answer 2

The variability we expect to see from one random sample to another. It is sometimes called sampling error, but sampling variability is the better term. ;)

Question 3

Sampling distribution model for a proportion

Answer 3

If assumptions of independence and random sampling are met, and we expect at least 10 success and 10 failures, then the sampling distribution of a proportion is modeled by a normal model with a mean equal to the true proportion value, p, and a standard deviation equal to square root of pq/n. ;)

Question 4

Central Limit Theorem

Answer 4

States that the sampling distribution model of the sample mean from a random sample is approximately normal for large n, regardless of the distribution of the population, as long as the observations are independent. ;)

Question 5

Sampling distribution model for a mean

Answer 5

If assumptions of independence, random, and large enough, the sampling distribution of the sample mean is modeled by a normal model with a mean equal to the population mean, u, and a standard deviation equal to o/sqr rt of n. ;)