1. Ch 11 實習 (2)

2. A Two - Tail Test Example 11.2
AT&T has been challenged by competitors who argued that their rates resulted in lower bills.
A statistics practitioner determines that the mean and standard deviation of monthly long-distance bills for all AT&T residential customers are $17.09 and $3.87 respectively.
Jia-Ying Chen

3. A Two - Tail Test Example 11.2 - continued
A random sample of 100 customers is selected and customers’ bills recalculated using a leading competitor’s rates (see Xm11-02).
Assuming the standard deviation is the same (3.87), can we infer that there is a difference between AT&T’s bills and the competitor’s bills (on the average)?
Jia-Ying Chen

4. A Two - Tail Test Solution H0: m = 17.09 Define the rejection region
Is the mean different from 17.09?
H0: m = 17.09
Define the rejection region
Jia-Ying Chen

5. A Two – Tail Test Solution - continued a/2 = 0.025 a/2 = 0.025 17.09
We want this erroneous rejection of H0 to be a rare event, say 5% chance.
If H0 is true (m =17.09), can still fall far above or far below 17.09, in which case we erroneously reject H0 in favor of H1
Jia-Ying Chen

6. A Two – Tail Test Solution - continued From the sample we have:
17.55
a/2 = 0.025
17.09
a/2 = 0.025
-za/2 = -1.96
za/2 = 1.96
Rejection region
Jia-Ying Chen

7. A Two – Tail Test
There is insufficient evidence to infer that there is a difference between the bills of AT&T and the competitor.
Also, by the p value approach:
The p-value = P(Z< -1.19)+P(Z >1.19)
= 2(.1173) = > .05
a/2 = 0.025
a/2 = 0.025
-1.19
1.19
-za/2 = -1.96
za/2 = 1.96
Jia-Ying Chen

8. Example 1
A random sample of 100 observations from a normal population whose standard deviation is 50 produced a mean of 75. Does this statistic provide sufficient evidence at the 5% level of significance to infer that the population mean is not 80?
Jia-Ying Chen

9. Solution H0: μ=80 vs. H1: μ ≠80 Rejection region: |z| > z0.025=1.96
Test statistic: z = (75-80)/(50/10)=-1.0
Conclusion: Don’t reject . No sufficient evidence at the 5% level of significance to infer that the population mean is not 80.
Jia-Ying Chen

10. Example 2
A machine that produces ball bearings is set so that the average diameter is 0.5 inch. A sample of 10 ball bearings was measured with the results shown here. Assuming that the standard deviation is 0.05 inch, can we conclude that at the 5% significance level that the mean diameter is not 0.5 inch?
Jia-Ying Chen

11. Solution
Jia-Ying Chen

12. Testing hypotheses and intervals estimators
Interval estimators can be used to test hypotheses.
Calculate the 1 - a confidence level interval estimator, then
if the hypothesized parameter value falls within the interval, do not reject the null hypothesis
if the hypothesized parameter value falls outside the interval, conclude that the null hypothesis can be rejected (m is not equal to the hypothesized value).
Jia-Ying Chen

13. Drawbacks
Two-tail interval estimators may not provide the right answer to the question posed in one-tail hypothesis tests.
The interval estimator does not yield a p-value.
Jia-Ying Chen

14. Example 3
Using a confidence interval when conducting a two-tail test for m, we do not reject H0 if the hypothesized value for m:
a. is to the left of the lower confidence limit (LCL).
b. is to the right of the upper confidence limit (UCL).
c. falls between the LCL and UCL.
d. falls in the rejection region.
Jia-Ying Chen

15. Calculation of the Probability of a Type II Error
To calculate Type II error we need to…
express the rejection region directly, in terms of the parameter hypothesized (not standardized).
specify the alternative value under H1.
型二誤差的定義是，H1 正確卻無法拒絕H0
在什麼規則下你無法拒絕H0 單尾 雙尾
Let us revisit Example 11.1
Jia-Ying Chen

16. Calculation of the Probability of a Type II Error
Express the rejection region directly, not in standardized terms
Let us revisit Example 11.1
The rejection region was with a = .05.
Let the alternative value be m = 180 (rather than just m>170)
H0: m = 170
H1: m = 180
a=.05
m= 170
m=180
Specify the alternative value under H1.
Do not reject H0
Jia-Ying Chen

17. Calculation of the Probability of a Type II Error
A Type II error occurs when a false H0 is not rejected.
A false H0…
H0: m = 170
…is not rejected
H1: m = 180
a=.05
m= 170
m=180
Jia-Ying Chen

18. Calculation of the Probability of a Type II Error
H0: m = 170
H1: m = 180
m=180
m= 170
Jia-Ying Chen

19. Example 4
A statistics practitioner wants to test the following hypotheses with σ=20 and n=100:
H0: μ=100
H1: μ>100
Using α=0.1 find the probability of a Type II error when μ=102
Jia-Ying Chen

20. Solution
Rejection region: z>zα
Jia-Ying Chen

21. Example 5
Calculate the probability of a Type II error for the following test of hypothesis, given that μ=203.
H0: μ=200
H1: μ≠200
α=0.05, σ=10, n=100
Jia-Ying Chen

22. Solution
Jia-Ying Chen

23. Effects on b of changing a
Decreasing the significance level a, increases the value of b, and vice versa.
a2 >
b2 < a1 b1
m= 170
m=180
Jia-Ying Chen

24. Judging the Test
A hypothesis test is effectively defined by the significance level a and by the sample size n.
If the probability of a Type II error b is judged to be too large, we can reduce it by
increasing a, and/or
increasing the sample size.
Jia-Ying Chen

25. Judging the Test Increasing the sample size reduces b
By increasing the sample size the standard deviation of the sampling distribution of the mean decreases. Thus, decreases.
Jia-Ying Chen

26. Judging the Test Increasing the sample size reduces b
Note what happens when n increases:
a does not change,
but b becomes smaller
m=180
m= 170
Jia-Ying Chen

27. Judging the Test Power of a test
The power of a test is defined as 1 - b.
It represents the probability of rejecting the null hypothesis when it is false.
Jia-Ying Chen

28. Example 6
For a given sample size n, if the level of significance α is decreased, the power of the test will:
a.increase.
b.decrease.
c.remain the same.
d.Not enough information to tell.
Jia-Ying Chen

29. Example 7
During the last energy crisis, a government official claimed that the average car owner refills the tank when there is more than 3 gallons left. To check the claim, 10 cars were surveyed as they entered a gas station. The amount of gas remaining before refill was measured and recorded as follows (in gallons): 3, 5, 3, 2, 3, 3, 2, 6, 4, and 1. Assume that the amount of gas remaining in tanks is normally distributed with a standard deviation of 1 gallon. Compute the probability of a Type II error and the power of the test if the true average amount of gas remaining in tanks is 3.5 gallons. (α=0.05)
Jia-Ying Chen

30. Solution H0: μ=3 H1: μ>3 Rejection region:z>zα
β = P( < 3.52 given that μ = 3.5) = P(z < 0.06) =
Power = 1 - β =
Jia-Ying Chen