Question 1

Which of the following best describes the cumulative area from the left for a standard normal distribution?

Accepted Answer

It is the area under the curve to the left of a specific z value.

Question 2

Which tool can be used to find the cumulative area from the left up to a specific z value in a standard normal distribution?

Accepted Answer

Statdisk

Question 3

When finding the area between two boundaries in a standard normal distribution, which of the following is correct?

Accepted Answer

The area is found by subtracting the cumulative area to the left of the lower boundary from the cumulative area to the left of the upper boundary.

Question 4

Which of the following software tools can be used to find the area between two boundaries in a standard normal distribution?

Accepted Answer

TI-83/84 Plus calculator

Question 5

Suppose you want to find $$P(a \u003c z \u003c b)$$ for a standard normal distribution. Which method would you use?

Accepted Answer

Find the cumulative area to the left of $$b$$ and subtract the cumulative area to the left of $$a.$$

Question 6

If you are using StatCrunch to find the cumulative area from the left for a z-score, what are you calculating?

Accepted Answer

The probability that $$z is $$less than a specific value

Question 7

Which of the following statements is true regarding the area under the standard normal curve?

Accepted Answer

The area to the left of $$z = 0 is $$exactly 0.5.

Question 8

A student wants to find the probability that a z-score is greater than a certain value. Which cumulative area should they use?

Accepted Answer

The cumulative area from the right of that z-score

Question 9

If the cumulative area to the left of $$z = 1.5 is 0.9332$$, what is the probability that $$z \u003e 1.5$$?

Accepted Answer

$$0.0668$$

Question 10

Which of the following best describes the area between two boundaries in a standard normal distribution?

Accepted Answer

It is the probability that the z-score falls between two specified values.

Normal Probability Distributions and Their Applications

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