2. Perbandingan dua populasi Pertemuan 8 Matakuliah: D0722 - Statistika dan Aplikasinya Tahun: 2010

3. 3 Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : 1.membandingkan perbedaan antara dua nilai tengah populasi bebas dan berpasangan 2.membandingkan perbedaan antara dua proporsi populasi dan dua ragam populasi Learning Outcomes

4. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-4 Using Statistics Paired-Observation Comparisons A Test for the Difference between Two Population Means Using Independent Random Samples A Large-Sample Test for the Difference between Two Population Proportions The F Distribution and a Test for the Equality of Two Population Variances Summary and Review of Terms The Comparison of Two Populations

5. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-5 Inferences about differences between parameters of two populations Paired-Observations same Observe the same group of persons or things –At two different times: “before” and “after” –Under two different sets of circumstances or “treatments” Independent Samples differentObserve different groups of persons or things –At different times or under different sets of circumstances 8-1 Using Statistics

6. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-6 Population parameters may differ at two different times or under two different sets of circumstances or treatments because: The circumstances differ between times or treatments The people or things in the different groups are themselves different By looking at paired-observations, we are able to minimize the “between group”, extraneous variation. Paired-Observation Comparisons

7. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-7 Paired-Observation Comparisons of Means

8. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-8 independent When paired data cannot be obtained, use independent random samples drawn at different times or under different circumstances. Large sample test if: Both n 1  30 and n 2  30 (Central Limit Theorem), or Both populations are normal and  1 and  2 are both known Small sample test if: Both populations are normal and  1 and  2 are unknown A Test for the Difference between Two Population Means Using Independent Random Samples

9. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-9 I: Difference between two population means is 0  1 =  2 H 0 :  1 -  2 = 0 H 1 :  1 -  2  0 II: Difference between two population means is less than 0  1   2 H 0 :  1 -  2  0 H 1 :  1 -  2  0 III: Difference between two population means is less than D  1   2 +D H 0 :  1 -  2  D H 1 :  1 -  2  D Comparisons of Two Population Means: Testing Situations

10. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-10 Large-sample test statistic for the difference between two population means: The term (  1 -  2 ) 0 is the difference between  1 an  2 under the null hypothesis. Is is equal to zero in situations I and II, and it is equal to the prespecified value D in situation III. The term in the denominator is the standard deviation of the difference between the two sample means (it relies on the assumption that the two samples are independent). Large-sample test statistic for the difference between two population means: The term (  1 -  2 ) 0 is the difference between  1 an  2 under the null hypothesis. Is is equal to zero in situations I and II, and it is equal to the prespecified value D in situation III. The term in the denominator is the standard deviation of the difference between the two sample means (it relies on the assumption that the two samples are independent). Comparisons of Two Population Means: Test Statistic

11. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-11 If we might assume that the population variances  1 2 and  2 2 are equal (even though unknown), then the two sample variances, s 1 2 and s 2 2, provide two separate estimators of the common population variance. Combining the two separate estimates into a pooled estimate should give us a better estimate than either sample variance by itself. x1x1 ************** } Deviation from the mean. One for each sample data point. Sample 1 From sample 1 we get the estimate s 1 2 with (n 1 -1) degrees of freedom. Deviation from the mean. One for each sample data point. ************** x2x2 } Sample 2 From sample 2 we get the estimate s 2 2 with (n 2 -1) degrees of freedom. From both samples together we get a pooled estimate, s p 2, with (n 1 -1) + (n 2 -1) = (n 1 + n 2 -2) total degrees of freedom. A Test for the Difference between Two Population Means: Assuming Equal Population Variances

12. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-12 A pooled estimate of the common population variance, based on a sample variance s 1 2 from a sample of size n 1 and a sample variance s 2 2 from a sample of size n 2 is given by: The degrees of freedom associated with this estimator is: df = (n 1 + n 2 -2) A pooled estimate of the common population variance, based on a sample variance s 1 2 from a sample of size n 1 and a sample variance s 2 2 from a sample of size n 2 is given by: The degrees of freedom associated with this estimator is: df = (n 1 + n 2 -2) The pooled estimate of the variance is a weighted average of the two individual sample variances, with weights proportional to the sizes of the two samples. That is, larger weight is given to the variance from the larger sample. Pooled Estimate of the Population Variance

13. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-13 Using the Pooled Estimate of the Population Variance

14. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-14 Hypothesized difference is zero I: Difference between two population proportions is 0 p 1 = p 2 » H 0 : p 1 -p 2 = 0 » H 1 : p 1 -p 2  0 II: Difference between two population proportions is less than 0 p 1  p 2 » H 0 : p 1 -p 2  0 » H 1 : p 1 -p 2 > 0 Hypothesized difference is other than zero: III: Difference between two population proportions is less than D p 1  p 2 +D » H 0 :p-p 2  D » H 1 : p 1 -p 2 > D 8-5 A Large-Sample Test for the Difference between Two Population Proportions

15. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-15 A large-sample test statistic for the difference between two population proportions, when the hypothesized difference is zero: where is the sample proportion in sample 1 and is the sample proportion in sample 2. The symbol stands for the combined sample proportion in both samples, considered as a single sample. That is: A large-sample test statistic for the difference between two population proportions, when the hypothesized difference is zero: where is the sample proportion in sample 1 and is the sample proportion in sample 2. The symbol stands for the combined sample proportion in both samples, considered as a single sample. That is: When the population proportions are hypothesized to be equal, then a pooled estimator of the proportion ( ) may be used in calculating the test statistic. Comparisons of Two Population Proportions When the Hypothesized Difference Is Zero: Test Statistic

16. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-16 Carry out a two-tailed test of the equality of banks’ share of the car loan market in 1980 and 1995. Comparisons of Two Population Proportions When the Hypothesized Difference Is Zero: Example 8-8

17. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-17 Carry out a one-tailed test to determine whether the population proportion of traveler’s check buyers who buy at least $2500 in checks when sweepstakes prizes are offered as at least 10% higher than the proportion of such buyers when no sweepstakes are on. Comparisons of Two Population Proportions When the Hypothesized Difference Is Not Zero: Example 8-9

18. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-18 The F distribution is the distribution of the ratio of two chi-square random variables that are independent of each other, each of which is divided by its own degrees of freedom. An F random variable with k 1 and k 2 degrees of freedom: The F Distribution and a Test for Equality of Two Population Variances

19. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-19 The F random variable cannot be negative, so it is bound by zero on the left. The F distribution is skewed to the right. The F distribution is identified the number of degrees of freedom in the numerator, k 1, and the number of degrees of freedom in the denominator, k 2. The F random variable cannot be negative, so it is bound by zero on the left. The F distribution is skewed to the right. The F distribution is identified the number of degrees of freedom in the numerator, k 1, and the number of degrees of freedom in the denominator, k 2. The F Distribution

20. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-20 Critical Points of the F Distribution Cutting Off a Right-Tail Area of 0.05 k 1 1 2 3 4 5 6 7 8 9 k 2 1161.4199.5215.7224.6230.2234.0236.8238.9240.5 218.5119.0019.1619.2519.3019.3319.3519.3719.38 310.139.559.289.129.018.948.898.858.81 47.716.946.596.396.266.166.096.046.00 56.615.795.415.195.054.954.884.824.77 65.995.144.764.534.394.284.214.154.10 75.594.744.354.123.973.873.793.733.68 85.324.464.073.843.693.583.503.443.39 95.124.263.863.633.483.373.293.233.18 104.964.103.713.483.333.223.143.073.02 114.843.983.593.363.203.09 3.01 2.952.90 124.753.893.493.263.113.002.912.852.80 134.673.813.413.183.032.922.832.772.71 144.603.743.343.112.962.852.762.702.65 154.543.683.293.062.902.792.712.642.59 3.01 543210 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 F 0.05 =3.01 f ( F ) F Distribution with 7 and 11 Degrees of Freedom F The left-hand critical point to go along with F (k1,k2) is given by: Where F (k1,k2) is the right-hand critical point for an F random variable with the reverse number of degrees of freedom. Using the Table of the F Distribution

21. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-21 I: Two-Tailed Test  1 =  2 H 0 :  1 =  2 H 1 :     2 II: One-Tailed Test  1  2 H 0 :  1  2 H 1 :  1  2 I: Two-Tailed Test  1 =  2 H 0 :  1 =  2 H 1 :     2 II: One-Tailed Test  1  2 H 0 :  1  2 H 1 :  1  2 Test Statistic for the Equality of Two Population Variances

22. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-22 The economist wants to test whether or not the event (interceptions and prosecution of insider traders) has decreased the variance of prices of stocks. Example

23. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 1-23 Example : Testing the Equality of Variances for Example 8-5

24. 24 RINGKASAN Pengujian hipotesis dua nilai tengah : Uji beda 2 nilai tengah populasi berpasangan Uji beda 2 nilai tengah populasi bebas Uji hipotesis dua proporsi dan ragam : Pengujian hipotesis dua proporsi Pengujian kesamaan ragam dua populasi