Q7. A Type II error:

A)   fails to reject a true null hypothesis.

B)   fails to reject a false null hypothesis.

C)   rejects a true null hypothesis.

Q8. If we fail to reject the null hypothesis when it is false, what type of error has occured?

A)   Type II.

B)   Type III.

C)   Type I.

Q9. Which of the following statements regarding hypothesis testing is least accurate?

A)   The significance level is the risk of making a type I error.

B)   A type I error is acceptance of a hypothesis that is actually false.

C)   A type II error is the acceptance of a hypothesis that is actually false.

Q10. A Type I error:

A)   rejects a false null hypothesis.

B)   rejects a true null hypothesis.

C)   fails to reject a false null hypothesis.

Q11. Which of the following statements regarding Type I and Type II errors is most accurate?

A)   A Type I error is failing to reject the null hypothesis when it is actually false.

B)   A Type I error is rejecting the null hypothesis when it is actually true.

C)   A Type II error is rejecting the alternative hypothesis when it is actually true.

Q12. A survey is taken to determine whether the average starting salaries of CFA charterholders is equal to or greater than $59,000 per year. What is the test statistic given a sample of 135 newly acquired CFA charterholders with a mean starting salary of $64,000 and a standard deviation of $5,500?

A)   10.56.

B)   -10.56.

C)   0.91.

答案和详解如下：

Q7. A Type II error:

A)   fails to reject a true null hypothesis.

B)   fails to reject a false null hypothesis.

C)   rejects a true null hypothesis.

Correct answer is B)

A Type II error is defined as accepting the null hypothesis when it is actually false. The chance of making a Type II error is called beta risk.

Q8. If we fail to reject the null hypothesis when it is false, what type of error has occured?

A)   Type II.

B)   Type III.

C)   Type I.

Correct answer is A)

A Type II error is defined as failing to reject the null hypothesis when it is actually false.

Q9. Which of the following statements regarding hypothesis testing is least accurate?

A)   The significance level is the risk of making a type I error.

B)   A type I error is acceptance of a hypothesis that is actually false.

C)   A type II error is the acceptance of a hypothesis that is actually false.

Correct answer is B)

A type I error is the rejection of a hypothesis that is actually true.

Q10. A Type I error:

A)   rejects a false null hypothesis.

B)   rejects a true null hypothesis.

C)   fails to reject a false null hypothesis.

Correct answer is B)

A Type I Error is defined as rejecting the null hypothesis when it is actually true. The probability of committing a Type I error is the significance level or alpha risk.

Q11. Which of the following statements regarding Type I and Type II errors is most accurate?

A)   A Type I error is failing to reject the null hypothesis when it is actually false.

B)   A Type I error is rejecting the null hypothesis when it is actually true.

C)   A Type II error is rejecting the alternative hypothesis when it is actually true.

Correct answer is B)

A Type I Error is defined as rejecting the null hypothesis when it is actually true. The probability of committing a Type I error is the risk level or alpha risk.

Q12. A survey is taken to determine whether the average starting salaries of CFA charterholders is equal to or greater than $59,000 per year. What is the test statistic given a sample of 135 newly acquired CFA charterholders with a mean starting salary of $64,000 and a standard deviation of $5,500?

A)   10.56.

B)   -10.56.

C)   0.91.

Correct answer is A)

With a large sample size (135) the z-statistic is used. The z-statistic is calculated by subtracting the hypothesized parameter from the parameter that has been estimated and dividing the difference by the standard error of the sample statistic. Here, the test statistic = (sample mean – hypothesized mean) / (population standard deviation / (sample size)^1/2) = (X − µ) / (σ / n^1/2) = (64,000 – 59,000) / (5,500 / 135^1/2) = (5,000) / (5,500 / 11.62) = 10.56.

谢谢了 哈哈