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# Section 10.2

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1. Section 10.2 Hypothesis Testing for Population Means (s Known)

2. Objectives Use the rejection region to draw a conclusion. Use the p-value to draw a conclusion.

3. Hypothesis Testing for Population Means (s Known) Test Statistic for a Hypothesis Test for a Population Mean (s Known) When the population standard deviation is known, the sample taken is a simple random sample, and either the sample size is at least 30 or the population distribution is approximately normal, the test statistic for a hypothesis test for a population mean is given by

4. Hypothesis Testing for Population Means (s Known) Test Statistic for a Hypothesis Test for a Population Mean (s Known) (cont.) where x̄ is the sample mean, m is the presumed value of the population mean from the null hypothesis, s is the population standard deviation, and n is the sample size.

5. Rejection Regions

6. Rejection Regions Decision Rule for Rejection Regions Reject the null hypothesis, H0, if the test statistic calculated from the sample data falls within the rejection region.

7. Rejection Regions Rejection Regions for Hypothesis Tests for Population Means (s Known)

8. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) The state education department is considering introducing new initiatives to boost the reading levels of fourth graders. The mean reading level of fourth graders in the state over the last 5 years was a Lexile reader measure of 800 L. (A Lexile reader measure is a measure of the complexity of the language that a reader is able to comprehend.) The developers of a new program claim that their techniques will raise the mean reading level of fourth graders by more than 50 L.

9. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) To assess the impact of their initiative, the developers were given permission to implement their ideas in the classrooms. At the end of the pilot study, a simple random sample of 1000 fourth graders had a mean reading level of 856 L. It is assumed that the population standard deviation is 98 L. Using a 0.05 level of significance, should the findings of the study convince the education department of the validity of the developers’ claim?

10. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) Solution Step 1: State the null and alternative hypotheses. The developers want to show that their classroom techniques will raise the fourth graders’ mean reading level to more than 850 L. This is written mathematically as m > 850, and since it is the research hypothesis, it will be Ha. The mathematical opposite is m ≤ 850. Thus, we have the following hypotheses.

11. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) Step 2: Determine which distribution to use for the test statistic, and state the level of significance. Note that the hypotheses are statements about the population mean, s is known, the sample is a simple random sample, and the sample size is at least 30. Thus, we will use a normal distribution, which means we need to use the z-test statistic.

12. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) In addition to determining which distribution to use for the test statistic, we need to state the level of significance. The problem states that a = 0.05. Step 3: Gather data and calculate the necessary sample statistics. At the end of the pilot study, a simple random sample of 1000 fourth graders had a mean reading level of 856 L. The population standard deviation is 98 L.

13. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) Thus, the test statistic is calculated as follows.

14. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) Step 4: Draw a conclusion and interpret the decision. Remember that we determine the type of test based on the alternative hypothesis. In this case, the alternative hypothesis contains “>,” which indicates that this is a right‑tailed test. To determine the rejection region, we need a z-value so that 0.05 of the area under the standard normal curve is to its right. If 0.05 of the area is to the right then 1 - 0.05 = 0.95 is the area to the left.

15. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) If we look up 0.9500 in the body of the cumulative z-table, the corresponding critical z-value is 1.645. Alternately, we can look up c = 0.95 in the table of critical z-values for rejection regions. Either way, the rejection region is z ≥ 1.645.

16. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.)

17. Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right‑Tailed, s Known) (cont.) The z-value of 1.94 falls in the rejection region. So the conclusion is to reject the null hypothesis. Thus the evidence collected suggests that the education department can be 95% sure of the validity of the developers’ claim that the mean Lexile reader measure of fourth graders will increase by more than 50 points.

18. p-Values p-value A p-value is the probability of obtaining a sample statistic as extreme or more extreme than the one observed in the data, when the null hypothesis, H0, is assumed to be true.

19. Example 10.11: Calculating the p-Value for a z-Test Statistic for a Left-Tailed Test Calculate the p-value for a hypothesis test with the following hypotheses. Assume that data have been collected and the test statistic was calculated to be z = -1.34.

20. Example 10.11: Calculating the p-Value for a z-Test Statistic for a Left-Tailed Test (cont.) Solution The alternative hypothesis tells us that this is a left-tailed test. Therefore, the p-value for this situation is the probability that z is less than or equal to -1.34, written p-value = P(z −1.34). Use a table or appropriate technology to find the area under the standard normal curve to the left of z = -1.34. Thus, the p-value ≈ 0.0901.

21. Example 10.11: Calculating the p-Value for a z-Test Statistic for a Left-Tailed Test (cont.)

22. Example 10.12: Calculating the p-Value for a z-Test Statistic for a Right-Tailed Test Calculate the p-value for a hypothesis test with the following hypotheses. Assume that data have been collected and the test statistic was calculated to be z = 2.78.

23. Example 10.12: Calculating the p-Value for a z-Test Statistic for a Right-Tailed Test (cont.) Solution The alternative hypothesis tells us that this is a right-tailed test. Therefore, the p-value for this situation is the probability that z is greater than or equal to 2.78, written p-value = P(z 2.78). Use a table or appropriate technology to find the area under the standard normal curve to the right of z = 2.78. Thus, the p-value ≈ 0.0027.

24. Example 10.12: Calculating the p-Value for a z-Test Statistic for a Right-Tailed Test (cont.)

25. Example 10.13: Calculating the p-Value for a z-Test Statistic for a Two-Tailed Test Calculate the p-value for a hypothesis test with the following hypotheses. Assume that data have been collected and the test statistic was calculated to be z = -2.15.

26. Example 10.13: Calculating the p-Value for a z-Test Statistic for a Two-Tailed Test (cont.) Solution The alternative hypothesis tells us that this is a two-tailed test. Thus, the p-value for this situation is the probability that z is either less than or equal to -2.15 or greater than or equal to 2.15, which is written mathematically as Use a table or appropriate technology to find the area under the standard normal curve to the left of z1 = −2.15. The area to the left of z1 = −2.15 is 0.0158.

27. Example 10.13: Calculating the p-Value for a z-Test Statistic for a Two-Tailed Test (cont.) Since the standard normal curve is symmetric about its mean, 0, the area to the right of z2 = 2.15 is also 0.0158. Thus, the p-value is calculated as follows.

28. Example 10.13: Calculating the p-Value for a z-Test Statistic for a Two-Tailed Test (cont.)

29. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator As we read in Example 10.10, the state education department is considering introducing new initiatives to boost the reading levels of fourth graders. The mean reading level of fourth graders in the state over the last 5 years was a Lexile reader measure of 800L. (A Lexile reader measure is a measure of the complexity of the language that a reader is able to comprehend.) The developers of a new program claim that their techniques will raise the mean reading level of fourth graders by more than 50L.

30. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) To assess the impact of their initiative, the developers were given permission to implement their ideas in the classrooms. At the end of the pilot study, a simple random sample of 1000 fourth graders had a mean reading level of 856L. It is assumed that the population standard deviation is 98L. Calculate the p-value for this hypothesis test.

31. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) Solution First, recall from Example 10.10 that the hypotheses are as follows. The alternative hypothesis tells us that this is a right-tailed test. Thus, in terms of the sampling distribution of sample means for a sample size of n = 1000, the

32. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) p-value for this situation is the probability of getting a sample mean greater than or equal to 856, written Press and then to get the DISTR (distributions) menu. Choose option 2:normalcdf(. Remember that you must enter the lower and upper bounds of the area under the curve as well as the mean and standard deviation of the normal distribution. For this example, the lower bound of the area is the sample mean, x̄ = 856.

33. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) Since we want the area to the right, the upper bound of the area is infinity, so we must enter a really large value of x̄. Thus, we will enter 1û99 for the upper bound. The mean of the sampling distribution is the hypothesized value of the population mean, The standard deviation of the sampling distribution is the population standard deviation divided by the square root of the sample size,

34. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) Enter normalcdf(856,1û99, 850, 98/ð (1000))and press .

35. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) The p-value returned is approximately 0.0264, as shown in the screenshot.

36. Example 10.14: Calculating the p-Value for a z-Test Statistic Using a TI-83/84 Plus Calculator (cont.) Note that by solving for the p-value in one step on the calculator, we eliminate any possible rounding errors. Thus, this method will always yield the most accurate p-value. Later in this chapter, we will see that the calculator can perform the entire hypothesis test in one step, and the output includes this p-value.

37. p-Values Conclusions Using p-Values • If p-value ≤ a, then reject the null hypothesis. • If p-value > a, then fail to reject the null hypothesis.

38. Example 10.15: Determining the Conclusion to a Hypothesis Test Using the p-Value For a certain hypothesis test, the p-value is calculated to be p-value = 0.0146. a. If the stated level of significance is 0.05, what is the conclusion to the hypothesis test? b. If the level of confidence is 99%, what is the conclusion to the hypothesis test? Solution a. The level of significance is a = 0.05. Next, note that 0.0146 < 0.05. Thus, p-value ≤ a, so we reject the null hypothesis.

39. Example 10.15: Determining the Conclusion to a Hypothesis Test Using the p-Value (cont.) b. The level of confidence is 99%, so the level of significance is calculated as follows. Next, note that 0.0146 > 0.01. Thus, p-value > a, so we fail to rejectthe null hypothesis.

40. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) A researcher claims that the mean age of women in California at the time of a first marriage is higher than 26.5 years. Surveying a simple random sample of 213 newlywed women in California, the researcher found a mean age of 27.0 years. Assuming that the population standard deviation is 2.3 years and using a 95% level of confidence, determine if there is sufficient evidence to support the researcher’s claim.

41. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Solution Step 1: State the null and alternative hypotheses. The researcher’s claim is investigating the mean age at first marriage for women in California. Therefore, the research hypothesis, Ha, is that the mean age is greater than 26.5, m > 26.5. The logical opposite is m ≤ 26.5. Thus, the null and alternative hypotheses are stated as follows.

42. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Step 2: Determine which distribution to use for the test statistic, and state the level of significance. Note that the hypotheses are statements about the population mean, the population standard deviation is known, the sample is a simple random sample, and the sample size is at least 30. Thus, we will use a normal distribution and calculate the z-test statistic.

43. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) We will draw a conclusion by computing the p-value for the calculated test statistic and comparing the value to a. For this hypothesis test, the level of confidence is 95%, so the level of significance is calculated as follows.

44. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Step 3: Gather data and calculate the necessary sample statistics. From the information given, we know that the presumed value of the population mean is m = 26.5, the sample mean is x̄ = 27.0, the population standard deviation is s = 2.3, and the sample size is n = 213.

45. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Thus, the test statistic is calculated as follows.

46. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Step 4: Draw a conclusion and interpret the decision. The alternative hypothesis tells us that we have a right-tailed test. Therefore, the p-value for this test statistic is the probability of obtaining a test statistic greater than or equal to z = 3.17, written as To find the p-value, we need to find the area under the standard normal curve to the right of z = 3.17.

47. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.)

48. Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, s Known) (cont.) Using a normal distribution table or appropriate technology, we find that the area is p-value ≈ 0.0008. Comparing this p-value to the level of significance, we see that 0.0008 < 0.05, so p-value ≤ a. Thus, the conclusion is to reject the null hypothesis. Therefore, we can say with 95% confidence that there is sufficient evidence to support the researcher’s claim that the mean age at first marriage for women in California is higher than 26.5 years.

49. Example 10.17: Performing a Hypothesis Test for a Population Mean (Two-Tailed, s Known) A recent study showed that the mean number of children for women in Europe is 1.5. A global watch group claims that German women have a mean fertility rate that is different from the mean for all of Europe. To test its claim, the group surveyed a simple random sample of 128 German women and found that they had a mean fertility rate of 1.4 children. The population standard deviation is assumed to be 0.8. Is there sufficient evidence to support the claim made by the global watch group at the 90% level of confidence?

50. Example 10.17: Performing a Hypothesis Test for a Population Mean (Two-Tailed, s Known) (cont.) Solution Step 1: State the null and alternative hypotheses. The watch group is investigating whether the mean fertility rate for German women is different from the mean for all of Europe. Thus, they need to find evidence that the mean fertility rate is not equal to 1.5.