Sampling Distributions Stats

What is central limit theorem?

• observation for center, spread, shape

• observation for the sample, spread, shape

• observation for the shape, size, and variation

Sample distribution for x is centered at the population median

• True

• False

As n gets larger, the spread of the sampling distribution of 𝑥̅ gets smaller....

• True

• False

Individual observations are more variable than sample means.....

• True

• False

What is the Law of Large Numbers?

• The sample mean (𝑥̅) is going to approach the true mean (𝜇) of the population as the number of observations decreases.

• The sample mean (𝑥̅) is not going to approach the true mean (𝜇) of the population as the number of observations decreases.

• The sample mean (𝑥̅) is going to approach the true mean (𝜇) of the population as the number of observations increases.

Which of the following is true in regards of how large n has to be?

• If the distribution of the X is skewed, then a sample size n of at least 25 or 30 yields an adequate approximation.

• If the distribution of the X is slightly skewed, then a sample size n of at least 25 or 30 yields an adequate approximation.

• If the distribution of the X is extremely skewed, then a sample size n of at least 25 or 30 yields an adequate approximation.

If the distribution of the X is extremely skewed.....

• then you may need an even smaller n

• then you may need an even larger n

• then your n is perfect

The sample distribution for sample mean (𝑥̅) is centered at the population mean.

• True

• False

As n gets smaller → the sampling distribution of the sample mean (𝑥̅) looks more normal and bell shaped

• True

• False

A distribution showing the sample means of all sample of size n of a population is called

• Normal Distribution

• Binomial Distribution

• Sampling Distribution

• Uniform Distribution