QBA EXAM 2

How many parameters are needed to fully describe any normal distribution?

2

Sampling distributions describe the distribution of... a. parameters b. both parameters and statistic c. statistics d. neither parameters nor statistics

C. statistics

In its standardized form, the normal distribution a. has a mean of 1 and a variance of 0 b. has an area equal to 0.5 c. cannot be used to approximate discrete probability distributions d. has a mean of 0 and a standard deviation of 1

D. has a mean of 0 and a standard deviation of 1.

Why is the Central Limit Theorem so important to the study of sampling distribution? a. it allows us to disregard the size of the sample selected when the population is not normal. b. it allows us to disregard the shape of the sampling distribution when the size of the sample population is large. c. it allows us to disregard the size of the population we are sampling from. d. it allows us the disregard the shape of the population when n is large.

D. it allows us the disregard the shape of the population when n is large.

The Central Limit Theorem is important in statistics because..

for a large n, it says the sampling distribution of the sample mean is approximately normal, regardless of the shape of population.