1. 1 Pertemuan 11 Sampling dan Sebaran Sampling-1 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1

2. 2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Menjelaskan pengertian dan tujuan sampling, serta dapat memberikan contoh tentang sampel statistik dan parameter populasi

3. 3 Outline Materi Sampel Statistik sebagai Estimator bagi Parameter Populasi Sebaran Sampling

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 5-4 l Using Statistics l Sample Statistics as Estimators of Population Parameters l Sampling Distributions l Estimators and Their Properties l Degrees of Freedom l Using the Computer l Summary and Review of Terms Sampling and Sampling Distributions 5

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 5-5 Statistical Inference: Predict and forecast values of population parameters... Test hypotheses about values of population parameters... Make decisions... On basis of sample statistics derived from limited and incomplete sample information Make generalizations about the characteristics of a population... On the basis of observations of a sample, a part of a population Inference 5-1 Statistics is a Science of Inference

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 5-6 Democrats Republicans People who have phones and/or cars and/or are Digest readers. Biased Sample Population Democrats Republicans Unbiased Sample Population Unbiased, representative sample drawn at random from the entire population. Biased, unrepresentative sample drawn from people who have cars and/or telephones and/or read the Digest. The Literary Digest Poll (1936)

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 5-7 estimator An estimator of a population parameter is a sample statistic used to estimate or predict the population parameter. estimate An estimate of a parameter is a particular numerical value of a sample statistic obtained through sampling. point estimate A point estimate is a single value used as an estimate of a population parameter. population parameter A population parameter is a numerical measure of a summary characteristic of a population. 5-2 Sample Statistics as Estimators of Population Parameters sample statisticA sample statistic is a numerical measure of a summary characteristic of a sample.

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 5-8 The sample mean,, is the most common estimator of the population mean,  The sample variance, s 2, is the most common estimator of the population variance,  2. The sample standard deviation, s, is the most common estimator of the population standard deviation, . The sample proportion,, is the most common estimator of the population proportion, p. The sample mean,, is the most common estimator of the population mean,  The sample variance, s 2, is the most common estimator of the population variance,  2. The sample standard deviation, s, is the most common estimator of the population standard deviation, . The sample proportion,, is the most common estimator of the population proportion, p. Estimators

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 5-9 population proportion The population proportion is equal to the number of elements in the population belonging to the category of interest, divided by the total number of elements in the population: sample proportion The sample proportion is the number of elements in the sample belonging to the category of interest, divided by the sample size: Population and Sample Proportions  p x n  p X N 

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 5-10 XXXX XXXXXX XXXX XXXXXX XXXXXX XXXXXX XXXX Population mean (  ) Sample points Frequency distribution of the population Sample mean ( ) A Population Distribution, a Sample from a Population, and the Population and Sample Means X

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 5-11 sampling distribution The sampling distribution of a statistic is the probability distribution of all possible values the statistic may assume, when computed from random samples of the same size, drawn from a specified population. sampling distribution of X The sampling distribution of X is the probability distribution of all possible values the random variable may assume when a sample of size n is taken from a specified population. 5-3 Sampling Distributions

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 5-12 Uniform population of integers from 1 to 8: XP(X)XP(X)(X-  x )(X-  x ) 2 P(X)(X-  x ) 2 10.1250.125-3.512.251.53125 20.1250.250-2.5 6.250.78125 30.1250.375-1.5 2.250.28125 40.1250.500-0.5 0.250.03125 50.1250.625 0.5 0.250.03125 60.1250.750 1.5 2.250.28125 70.1250.875 2.5 6.250.78125 80.1251.000 3.512.251.53125 1.0004.500 5.25000 87654321 0.2 0.1 0.0 X P ( X ) Uniform Distribution (1,8) E(X) =  = 4.5 V(X) =  2 = 5.25 SD(X) =  = 2.2913 Sampling Distributions (Continued)

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 5-13 There are 8*8 = 64 different but equally-likely samples of size 2 that can be drawn (with replacement) from a uniform population of the integers from 1 to 8: Each of these samples has a sample mean. For example, the mean of the sample (1,4) is 2.5, and the mean of the sample (8,4) is 6. Sampling Distributions (Continued)

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 5-14 Sampling Distribution of the Mean sampling distribution of the the sample mean The probability distribution of the sample mean is called the sampling distribution of the the sample mean. X P(X) XP(X)X-  X (X-  X ) 2 P(X)(X-  X ) 2 1.00.0156250.015625-3.512.250.191406 1.50.0312500.046875-3.0 9.000.281250 2.00.0468750.093750-2.5 6.250.292969 2.50.0625000.156250-2.0 4.000.250000 3.00.0781250.234375-1.5 2.250.175781 3.50.0937500.328125-1.0 1.000.093750 4.00.1093750.437500-0.5 0.250.027344 4.50.1250000.562500 0.0 0.000.000000 5.00.1093750.546875 0.5 0.250.027344 5.50.0937500.515625 1.0 1.000.093750 6.00.0781250.468750 1.5 2.250.175781 6.50.0625000.406250 2.0 4.000.250000 7.00.0468750.328125 2.5 6.250.292969 7.50.0312500.234375 3.0 9.000.281250 8.00.0156250.125000 3.512.250.191406 1.0000004.5000002.625000 EX VX SDX X X X () () ().       4.5 2.625 16202 2 Sampling Distributions (Continued)

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 5-15 Comparing the population distribution and the sampling distribution of the mean: The sampling distribution is more bell-shaped and symmetric. Both have the same center. The sampling distribution of the mean is more compact, with a smaller variance. 87654321 0.2 0.1 0.0 X P ( X ) Uniform Distribution (1,8) X Properties of the Sampling Distribution of the Sample Mean

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 5-16 expected value of the sample mean The expected value of the sample mean is equal to the population mean: variance of the sample mean The variance of the sample mean is equal to the population variance divided by the sample size: standard deviation of the sample mean, known as the standard error of the mean The standard deviation of the sample mean, known as the standard error of the mean, is equal to the population standard deviation divided by the square root of the sample size: Relationships between Population Parameters and the Sampling Distribution of the Sample Mean

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 5-17 normal population normal sampling distribution When sampling from a normal population with mean  and standard deviation , the sample mean, X, has a normal sampling distribution: This means that, as the sample size increases, the sampling distribution of the sample mean remains centered on the population mean, but becomes more compactly distributed around that population mean Normal population 0.4 0.3 0.2 0.1 0.0 f ( X ) Sampling Distribution of the Sample Mean  Sampling Distribution: n =2 Sampling Distribution: n =16 Sampling Distribution: n =4 Sampling from a Normal Population Normal population

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 5-18 When sampling from a population with mean  and finite standard deviation , the sampling distribution of the sample mean will tend to a normal distribution with mean  and standard deviation as the sample size becomes large (n >30). For “large enough” n: When sampling from a population with mean  and finite standard deviation , the sampling distribution of the sample mean will tend to a normal distribution with mean  and standard deviation as the sample size becomes large (n >30). For “large enough” n: P ( X ) X 0.25 0.20 0.15 0.10 0.05 0.00 n = 5 P ( X ) 0.2 0.1 0.0 X n = 20 f ( X ) X - 0.4 0.3 0.2 0.1 0.0  Large n The Central Limit Theorem

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 5-19 NormalUniformSkewed Population n = 2 n = 30 X  X  X  X  General Any The Central Limit Theorem Applies to Sampling Distributions from Any Population

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 5-20 Mercury makes a 2.4 liter V-6 engine, the Laser XRi, used in speedboats. The company’s engineers believe the engine delivers an average power of 220 horsepower and that the standard deviation of power delivered is 15 HP. A potential buyer intends to sample 100 engines (each engine is to be run a single time). What is the probability that the sample mean will be less than 217HP? The Central Limit Theorem (Example 5-1)

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 5-21 Example 5-2

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 5-22 unknown If the population standard deviation, , is unknown, replace  with the sample standard deviation, s. If the population is normal, the resulting statistic: t distribution with (n - 1) degrees of freedom has a t distribution with (n - 1) degrees of freedom. unknown If the population standard deviation, , is unknown, replace  with the sample standard deviation, s. If the population is normal, the resulting statistic: t distribution with (n - 1) degrees of freedom has a t distribution with (n - 1) degrees of freedom. 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. The variance of t 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. The variance of t 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. Standard normal t, df=20 t, df=10  Student’s t Distribution

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 5-23 sample proportion The sample proportion is the percentage of successes in n binomial trials. It is the number of successes, X, divided by the number of trials, n. normal distribution As the sample size, n, increases, the sampling distribution of approaches a normal distribution with mean p and standard deviation Sample proportion: 1514131211109876543210 0.2 0.1 0.0 P ( X ) n=15, p = 0.3 X 14 15 13 15 12 15 11 15 10 15 9 15 8 15 7 15 6 15 5 15 4 15 3 15 2 15 1 15 0 15 ^p^p 210 0.5 0.4 0.3 0.2 0.1 0.0 X P ( X ) n=2, p = 0.3 109876543210 0.3 0.2 0.1 0.0 P ( X ) n=10,p=0.3 X The Sampling Distribution of the Sample Proportion,

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 5-24 In recent years, convertible sports coupes have become very popular in Japan. Toyota is currently shipping Celicas to Los Angeles, where a customizer does a roof lift and ships them back to Japan. Suppose that 25% of all Japanese in a given income and lifestyle category are interested in buying Celica convertibles. A random sample of 100 Japanese consumers in the category of interest is to be selected. What is the probability that at least 20% of those in the sample will express an interest in a Celica convertible? n p npEp pp n Vp pp n SDp        100 025 100025 1 75 100 0001875 1 0 004330127. ()(.)(  ) () (.)(.).(  ) ()..(  ) PpP pp pp n p pp n PzPz Pz ( .)  (). ().. (.)(.).. (.).                                      020 1 1 25 75 100 05 0433 11508749 Sample Proportion (Example 5-3)

25. 25 Penutup Pembahasan materi dilanjutkan dengan Materi Pokok 12 (Sampling dan Sebaran Sampling-2)