1. STATISTICAL INFERENCE PART VIII HYPOTHESIS TESTING - APPLICATIONS – TWO POPULATION TESTS 1

2. INFERENCE ABOUT THE DIFFERENCE BETWEEN TWO SAMPLES INDEPENDENT SAMPLES POPULATION 1POPULATION 2 PARAMETERS:  1, PARAMETERS:  2, Statistics: Sample size: n 1 Sample size: n 2 2

3. SAMPLING DISTRIBUTION OF Consider random samples of n 1 and n 2 from two normal populations. Then, For non-normal distributions, we can use Central Limit Theorem for n 1  30 and n 2  30. 3

4. CONFIDENCE INTERVAL FOR  1 -  2  1 AND  2 ARE KNOWN FOR NORMAL DISTRIBUTION OR LARGE SAMPLE A 100(1-  C.I. for      is given by: If   and    are unknown and unequal, we can replace them with s 1 and s 2. 4 We are still using Z table because this is large sample or normal distribution situation.

5. EXAMPLE Set up a 95% CI for  2 -  1. 95% CI for  2 -  1 : 5 Is there any significant difference between mean family incomes of two groups?

6. INTERPRETATION With 95% confidence, mean family income in the second group may exceed that in the first group by between $363 and $2397. 6

7. Test Statistic for  1 -  2 when  1 and  2 are known Test statistic: If   and    are unknown and unequal, we can replace them with s 1 and s 2. 7

8. EXAMPLE Two different procedures are used to produce battery packs for laptop computers. A major electronics firm tested the packs produced by each method to determine the number of hours they would last before final failure. The electronics firm wants to know if there is a difference in the mean time before failure of the two battery packs.  =0.10 8

9. SOLUTION STEP 1: H 0 :  1 =  2  H 0 :  1 -  2 = 0 H A :  1   2  H A :  1 -  2  0 STEP 2: Test statistic: STEP 3: Decision Rule = Reject H 0 if z z  /2 =1.645. STEP 4: Not reject H 0. There is not sufficient evidence to conclude that there is a difference in the mean life of the 2 types of battery packs. 9

10.  1 AND  2 ARE UNKNOWN if  1 =  2 A 100(1-  C.I. for      is given by: where 10

11. Test Statistic for  1 -  2 when  1 =  2 and unknown Test Statistic: where 11

12. EXAMPLE The statistics obtained from random sampling are given as It is thought that  1 <  2. Test the appropriate hypothesis assuming normality with  = 0.01. 12

13. SOLUTION n 1 < 30 and n 2 < 30  t-test Because s 1 and s 2 are not much different from each other, use equal-variance t-test (More formally, we can test Ho: σ² 1 =σ² 2 ). H 0 :  1 =  2 H A :  1 <  2 (  1 -  2 <0) 13

14. Decision Rule: Reject H 0 if t < -t 0.01,8+9-2 =-2.602 Conclusion: Since t = -19.13 < -t 0.01,8+9-2 =-2.602, reject H 0 at  = 0.01. 14

15. Test Statistic for  1 -  2 when  1  2 and unknown Test Statistic: with the degree of freedom 15

16. EXAMPLE Does consuming high fiber cereals entail weight loss? 30 people were randomly selected and asked what they eat for breakfast and lunch. They were divided into those consuming and those not consuming high fiber cereals. The statistics are obtained as n 1 =10;n 2 =20 16

17. SOLUTION Because s 1 and s 2 are too different from each other and the population variances are not assumed equal, we can use a t statistic with degrees of freedom 17

18. H 0 :  1 -  2 = 0 H A :  1 -  2 < 0 DECISION RULE: Reject H 0 if t < -t ,df = -t 0.05, 25 = -1.708. CONCLUSION: Since t =-2.31< -t 0.05, 25 =-1.708, reject H 0 at  = 0.05. 18

19. MINITAB OUTPUT Two Sample T-Test and Confidence Interval Twosample T for Consmers vs Non-cmrs N Mean StDev SE Mean Consmers 10 595.8 35.7 11 Non-cmrs 20 661 116 26 95% C.I. for mu Consmers - mu Non-cmrs: ( -123, -7) T-Test mu Consmers = mu Non-cmrs (vs <): T= -2.31 P=0.015 DF= 25 19

20. Inference about the Difference of Two Means: Matched Pairs Experiment Data are generated from matched pairs; not independent samples. Let X i and Y i denote the measurements for the i-th subject. Thus, (X i, Y i ) is a matched pair observations. Denote D i = Y i -X i or X i -Y i. If there are n subjects studied, we have D 1, D 2,…, D n. Then,

21. CONFIDENCE INTERVAL FOR  D =  1 -  2 A 100(1-  C.I. for  D =      is given by: For n  30, we can use z instead of t.

22. HYPOTHESIS TESTS FOR  D =  1 -  2 The test statistic for testing hypothesis about  D  is given by with degree of freedom n-1.

23. EXAMPLE Sample data on attitudes before and after viewing an informational film. iXiXi YiYi D i =Y i -X i

24. 90% CI for  D =  1 -  2 : With 90% confidence, the mean attitude measurement after viewing the film exceeds the mean attitude measurement before viewing by between 0.36 and 14.92 units. t 0.05, 9

25. EXAMPLE How can we design an experiment to show which of two types of tires is better? Install one type of tire on one wheel and the other on the other (front) wheels. The average tire (lifetime) distance (in 1000’s of miles) is: with a sample difference s.d. of There are a total of n=20 observations

26. SOLUTION H 0 :  D =0 H A :  D >0 Test Statistics: Reject H 0 if t>t.05,19 =1.729, Conclusion: Reject H 0 at  =0.05

27. Inference About the Difference of Two Population Proportions Population 1 Population 2 PARAMETERS: p 1 PARAMETERS: p 2 Statistics: Sample size: n 1 Sample size: n 2 Independent populations

28. SAMPLING DISTRIBUTION OF A point estimator of p 1 -p 2 is The sampling distribution of is if n i p i  5 and n i q i  5, i=1,2.

29. STATISTICAL TESTS Two-tailed test H o :p 1 =p 2 H A :p 1  p 2 Reject H 0 if z z  /2. One-tailed tests H o :p 1 =p 2 H A :p 1 > p 2 Reject H 0 if z > z  H o :p 1 =p 2 H A :p 1 < p 2 Reject H 0 if z < -z 

30. EXAMPLE A manufacturer claims that compared with his closest competitor, fewer of his employees are union members. Of 318 of his employees, 117 are unionists. From a sample of 255 of the competitor’s labor force, 109 are union members. Perform a test at  = 0.05.

31. SOLUTION H 0 : p 1 = p 2 H A : p 1 < p 2 and, so pooled sample proportion is Test Statistic:

32. Decision Rule: Reject H 0 if z < -z 0.05 =-1.96. Conclusion: Because z = -1.4518 > -z 0.05 =- 1.96, not reject H 0 at  =0.05. Manufacturer is wrong. There is no significant difference between the proportions of union members in these two companies.

33. Example In a study, doctors discovered that aspirin seems to help prevent heart attacks. Half of 22,000 male participants took aspirin and the other half took a placebo. After 3 years, 104 of the aspirin and 189 of the placebo group had heart attacks. Test whether this result is significant. p 1 : proportion of all men who regularly take aspirin and suffer from heart attack. p 2 : proportion of all men who do not take aspirin and suffer from heart attack

34. Test of Hypothesis for p 1 -p 2 H 0 : p 1 -p 2 = 0 H A : p 1 -p 2 < 0 Test Statistic: Conclusion: Reject H 0 since p-value=P(z<-5.02)  0

35. Confidence Interval for p 1 -p 2 A 100(1-  C.I. for p   p  is given by:

36. Inference About Comparing Two Population Variances Population 1 Population 2 PARAMETERS: Statistics: Sample size: n 1 Sample size: n 2 Independent populations

37. SAMPLING DISTRIBUTION OF For independent r.s. from normal populations (1-α)100% CI for

38. STATISTICAL TESTS Two-tailed test H o :(or ) H A : Reject H 0 if F F 1-  /2,n 1 -1,n 2 -1. One-tailed tests H o : H A : Reject H 0 if F > F 1- , n 1 -1,n 2 -1 H o : H A : Reject H 0 if F < F ,n 1 -1,n 2 -1

39. Example A scientist would like to know whether group discussion tends to affect the closeness of judgments by property appraisers. Out of 16 appraisers, 6 randomly selected appraisers made decisions after a group discussion, and rest of them made their decisions without a group discussion. As a measure of closeness of judgments, scientist used variance. Hypothesis: Groups discussion will reduce the variability of appraisals. 39

40. Example, cont. Appraisal values (in thousand $)Statistics With discussion97, 101,102,95,98,103n 1 =6 s 1 ²=9.867 Without discussion118, 109, 84, 85, 100, 121, 115, 93, 91, 112 n 2 =10 s 2 ²=194.18 40 Ho: versus H 1 : Reject Ho. Group discussion reduces the variability in appraisals.