1. 1 Pertemuan 13 Selang Kepercayaan-1 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1

2. 2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Menjelaskan pengertian selang kepercayaan dan penerapannya bagi berbagai kondisi populasi

3. 3 Outline Materi Selang Kepercayaan bagi μ ketika σ diketahui Selang Kepercayaan bagi μ ketika σ tidak diketahui

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 6-4 l Using Statistics l Confidence Interval for the Population Mean When the Population Standard Deviation is Known Confidence Intervals for  When  is Unknown - The t Distribution l Large-Sample Confidence Intervals for the Population Proportion p l Confidence Intervals for the Population Variance l Sample Size Determination l Summary and Review of Terms Confidence Intervals 6

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 6-5 Consider the following statements: x = 550 A single-valued estimate that conveys little information about the actual value of the population mean. We are 99% confident that  is in the interval [449,551] An interval estimate which locates the population mean within a narrow interval, with a high level of confidence. We are 90% confident that  is in the interval [400,700] An interval estimate which locates the population mean within a broader interval, with a lower level of confidence. 6-1 Introduction

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 6-6 Point Estimate A single-valued estimate. A single element chosen from a sampling distribution. Conveys little information about the actual value of the population parameter, about the accuracy of the estimate. Confidence Interval or Interval Estimate An interval or range of values believed to include the unknown population parameter. confidence Associated with the interval is a measure of the confidence we have that the interval does indeed contain the parameter of interest. Types of Estimators

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 6-7 A confidence interval or interval estimate is a range or interval of numbers believed to include an unknown population parameter. Associated with the interval is a measure of the confidence we have that the interval does indeed contain the parameter of interest. A confidence interval or interval estimate is a range or interval of numbers believed to include an unknown population parameter. Associated with the interval is a measure of the confidence we have that the interval does indeed contain the parameter of interest. A confidence interval or interval estimate has two components: A range or interval of values An associated level of confidence Confidence Interval or Interval Estimate

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 6-8 l If the population distribution is normalthe sampling distribution of the mean is normal. l If the population distribution is normal, the sampling distribution of the mean is normal. If the sample is sufficiently large, regardless of the shape of the population distributionthe sampling distribution is normal If the sample is sufficiently large, regardless of the shape of the population distribution, the sampling distribution is normal (Central Limit Theorem). 43210-1-2-3-4 0.4 0.3 0.2 0.1 0.0 z f ( z ) Standard Normal Distribution: 95% Interval 6-2 Confidence Interval for  When  Is Known

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 6-9 6-2 Confidence Interval for  when  is Known (Continued)

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 6-10 Approximately 95% of sample means can be expected to fall within the interval. Conversely, about 2.5% can be expected to be above and 2.5% can be expected to be below. So 5% can be expected to fall outside the interval. Approximately 95% of sample means can be expected to fall within the interval. Conversely, about 2.5% can be expected to be above and 2.5% can be expected to be below. So 5% can be expected to fall outside the interval. 0.4 0.3 0.2 0.1 0.0 x f ( x ) Sampling Distribution of the Mean  x x x x x x x x 2.5% 95% 2.5% x 2.5% fall above the interval 2.5% fall below the interval 95% fall within the interval A 95% Interval around the Population Mean

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 6-11 Approximately 95% of the intervals around the sample mean can be expected to include the actual value of the population mean, . (When the sample mean falls within the 95% interval around the population mean.) not * 5% of such intervals around the sample mean can be expected not to include the actual value of the population mean. (When the sample mean falls outside the 95% interval around the population mean.) Approximately 95% of the intervals around the sample mean can be expected to include the actual value of the population mean, . (When the sample mean falls within the 95% interval around the population mean.) not * 5% of such intervals around the sample mean can be expected not to include the actual value of the population mean. (When the sample mean falls outside the 95% interval around the population mean.) x x  x  95% Intervals around the Sample Mean 0.4 0.3 0.2 0.1 0.0 x f ( x ) Sampling Distribution of the Mean  x x x x x x x x 2.5% 95% 2.5% x x  x  * *

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 6-12 A 95% confidence interval for  when  is known and sampling is done from a normal population, or a large sample is used: The quantity is often called the margin of error or the sampling error. For example, if:n = 25  = 20 = 122 A 95% confidence interval: The 95% Confidence Interval for 

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 6-13 We define as the z value that cuts off a right-tail area of under the standard normal curve. (1-  ) is called the confidence coefficient.  is called the error probability, and (1-  )100% is called the confidence level. Pzz Pzz Pzzz z n                                 2 2 22 2 1() (1-)100% Confidence Interval: x 543210-1-2-3-4-5 0.4 0.3 0.2 0.1 0.0 Z f ( z ) Standard Normal Distribution A (1-  )100% Confidence Interval for  

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 6-14 Critical Values of z and Levels of Confidence 543210-1-2-3-4-5 0.4 0.3 0.2 0.1 0.0 Z f ( z ) Standard Normal Distribution

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 6-15 When sampling from the same population, using a fixed sample size, the higher the confidence level, the wider the confidence interval. 543210-1-2-3-4-5 0.4 0.3 0.2 0.1 0.0 Z f ( z ) Standard Normal Distribution 543210-1-2-3-4-5 0.4 0.3 0.2 0.1 0.0 Z f ( z ) Standard Normal Distribution The Level of Confidence and the Width of the Confidence Interval

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 6-16 The Sample Size and the Width of the Confidence Interval When sampling from the same population, using a fixed confidence level, the larger the sample size, n, the narrower the confidence interval. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 x f ( x ) Sampling Distribution of the Mean 95% Confidence Interval: n = 40 0.4 0.3 0.2 0.1 0.0 x f ( x ) Sampling Distribution of the Mean 95% Confidence Interval: n = 20

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 6-17 Population consists of the Fortune 500 Companies (Fortune Web Site), as ranked by Revenues. You are trying to to find out the average Revenues for the companies on the list. The population standard deviation is $15,056.37. A random sample of 30 companies obtains a sample mean of $10,672.87. Give a 95% and 90% confidence interval for the average Revenues. Example 6-1

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 6-18 Example 6-1 (continued) - Using the Template Note:The remaining part of the template display is shown on the next slide. Note: The remaining part of the template display is shown on the next slide.

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 6-19 Example 6-1 (continued) - Using the Template  (Sigma)

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 6-20 Example 6-1 (continued) - Using the Template when the Sample Data is Known

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 6-21 The t is a family of bell-shaped and symmetric distributions, one for each number of degree of freedom. The expected value of t is 0. For df > 2, the variance of t is df/(df-2). This is greater than 1, but approaches 1 as the number of degrees of freedom increases. The t is flatter and has fatter tails than does the standard normal. The t distribution approaches a standard normal as the number of degrees of freedom increases The t is a family of bell-shaped and symmetric distributions, one for each number of degree of freedom. The expected value of t is 0. For df > 2, the variance of t is df/(df-2). This is greater than 1, but approaches 1 as the number of degrees of freedom increases. The t is flatter and has fatter tails than does the standard normal. The t distribution approaches a standard normal as the number of degrees of freedom increases If the population standard deviation, , is not known, replace  with the sample standard deviation, s. If the population is normal, the resulting statistic: has a t distribution with (n - 1) degrees of freedom. Standard normal t, df = 20 t, df = 10  6-3 Confidence Interval or Interval Estimate for  When  Is Unknown - The t Distribution

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 6-22 The t Distribution Template

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 6-23 A (1-  )100% confidence interval for  when  is not known (assuming a normally distributed population): where is the value of the t distribution with n-1 degrees of freedom that cuts off a tail area of to its right. A (1-  )100% confidence interval for  when  is not known (assuming a normally distributed population): where is the value of the t distribution with n-1 degrees of freedom that cuts off a tail area of to its right. 6-3 Confidence Intervals for  when  is Unknown- The t Distribution

24. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 6-24 df t 0.100 t 0.050 t 0.025 t 0.010 t 0.005 ------------------------------- 13.0786.31412.70631.82163.657 21.8862.9204.3036.9659.925 31.6382.3533.1824.5415.841 41.5332.1322.7763.7474.604 51.4762.0152.5713.3654.032 61.4401.9432.4473.1433.707 71.4151.8952.3652.9983.499 81.3971.8602.3062.8963.355 91.3831.8332.2622.8213.250 101.3721.812 2.2282.7643.169 111.3631.7962.2012.7183.106 121.3561.7822.1792.6813.055 131.3501.7712.1602.6503.012 141.3451.7612.1452.6242.977 151.3411.7532.1312.6022.947 161.3371.7462.1202.5832.921 171.3331.7402.1102.5672.898 181.3301.7342.1012.5522.878 191.3281.7292.0932.5392.861 201.3251.7252.0862.5282.845 211.3231.7212.0802.5182.831 221.3211.7172.0742.5082.819 231.3191.7142.0692.5002.807 241.3181.7112.0642.4922.797 251.3161.7082.0602.4852.787 261.3151.7062.0562.4792.779 271.3141.7032.0522.4732.771 281.3131.7012.0482.4672.763 291.3111.6992.0452.4622.756 301.3101.6972.0422.4572.750 401.3031.6842.0212.4232.704 601.2961.6712.0002.3902.660 1201.2891.6581.9802.3582.617 1.2821.6451.9602.3262.576 0 0.4 0.3 0.2 0.1 0.0 t f ( t ) t Distribution: df=10 Area = 0.10 } } Area = 0.025 } } 1.372 -1.372 2.228 -2.228 Whenever  is not known (and the population is assumed normal), the correct distribution to use is the t distribution with n-1 degrees of freedom. Note, however, that for large degrees of freedom, the t distribution is approximated well by the Z distribution. The t Distribution

25. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 6-25 A stock market analyst wants to estimate the average return on a certain stock. A random sample of 15 days yields an average (annualized) return of and a standard deviation of s = 3.5%. Assuming a normal population of returns, give a 95% confidence interval for the average return on this stock. The critical value of t for df = (n -1) = (15 -1) =14 and a right-tail area of 0.025 is: The corresponding confidence interval or interval estimate is: Example 6-2 df t 0.100 t 0.050 t 0.025 t 0.010 t 0.005 ------------------------------- 13.0786.31412.70631.82163.657...... 131.3501.7712.1602.6503.012 141.3451.7612.1452.6242.977 151.3411.7532.1312.6022.947......

26. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 6-26 Whenever  is not known (and the population is assumed normal), the correct distribution to use is the t distribution with n-1 degrees of freedom. Note, however, that for large degrees of freedom, the t distribution is approximated well by the Z distribution. df t 0.100 t 0.050 t 0.025 t 0.010 t 0.005 ------------------------------- 13.0786.31412.70631.82163.657...... 1201.2891.6581.9802.3582.617 1.2821.6451.9602.3262.576 Large Sample Confidence Intervals for the Population Mean

27. COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 6-27 Example 6-3: Example 6-3: An economist wants to estimate the average amount in checking accounts at banks in a given region. A random sample of 100 accounts gives x-bar = $357.60 and s = $140.00. Give a 95% confidence interval for , the average amount in any checking account at a bank in the given region. Large Sample Confidence Intervals for the Population Mean

28. 28 Penutup Pembahasan materi dilanjutkan dengan Materi Pokok 14 (Selang Kepercayaan-2)