1. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-1 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

2. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-2 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 5-1 Statistics is a Science of Inference

3. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-3 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)

4. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-4 An estimator of a population parameter is a sample statistic used to estimate or predict the population parameter. An estimate of a parameter is a particular numerical value of a sample statistic obtained through sampling. A point estimate is a single value used as an estimate of a 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 A sample statistic is a numerical measure of a summary characteristic of a sample.

5. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-5 The sample mean, X, 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, p, is the most common estimator of the population proportion, p. The sample mean, X, 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, p, is the most common estimator of the population proportion, p. ^ Estimators

6. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-6 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: 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

7. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-7 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

8. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-8 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. 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 (1)

9. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-9 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 (2)

10. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-10 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 (3)

11. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-11 Sampling Distribution of the 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 (4)

12. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-12 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

13. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-13 The expected value of the sample mean is equal to the population mean: The variance of the sample mean is equal to the population variance divided by the sample size: 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

14. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-14 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

15. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-15 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

16. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-16 NormalUniformSkewed Population n = 2 n = 30 X  X  X  X  General The Central Limit Theorem Applies to Sampling Distributions from Any Population

17. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-17 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)

18. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-18 0 5 10 15 20 25 Frequency 2.00 - 2.49 3.00 - 3.494.00 - 4.49 5.00 - 5.49 6.00 - 6.497.00 - 7.49 Range EPS Mean Distribution Excel Output (Example 5-2)

19. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-19 If the population standard deviation, , is unknown, 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. If the population standard deviation, , is unknown, 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. 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

20. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-20 The sample proportion is the percentage of successes in n binomial trials. I t is the number of successes, X, divided by the number of trials, n. 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,

21. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-21 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)

22. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-22 An estimator of a population parameter is a sample statistic used to estimate the parameter. The most commonly-used estimator of the: Population Parameter Sample Statistic Mean (  ) is theMean (X) Variance (  2 )is the Variance (s 2 ) Standard Deviation (  )is the Standard Deviation (s) Proportion (p)is the Proportion ( ) An estimator of a population parameter is a sample statistic used to estimate the parameter. The most commonly-used estimator of the: Population Parameter Sample Statistic Mean (  ) is theMean (X) Variance (  2 )is the Variance (s 2 ) Standard Deviation (  )is the Standard Deviation (s) Proportion (p)is the Proportion ( ) Desirable properties of estimators include: – Unbiasedness – Efficiency – Consistency – Sufficiency Desirable properties of estimators include: – Unbiasedness – Efficiency – Consistency – Sufficiency 5-4 Estimators and Their Properties

23. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-23 An estimator is said to be unbiased if its expected value is equal to the population parameter it estimates. For example, E(X)=  so the sample mean is an unbiased estimator of the population mean. Unbiasedness is an average or long-run property. The mean of any single sample will probably not equal the population mean, but the average of the means of repeated independent samples from a population will equal the population mean. Any systematic deviation of the estimator from the population parameter of interest is called a bias. An estimator is said to be unbiased if its expected value is equal to the population parameter it estimates. For example, E(X)=  so the sample mean is an unbiased estimator of the population mean. Unbiasedness is an average or long-run property. The mean of any single sample will probably not equal the population mean, but the average of the means of repeated independent samples from a population will equal the population mean. Any systematic deviation of the estimator from the population parameter of interest is called a bias. Unbiasedness

24. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-24 An unbiased estimator is on target on average. A biased estimator is off target on average. { Bias Unbiased and Biased Estimators

25. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-25 An estimator is efficient if it has a relatively small variance (and standard deviation). An efficient estimator is, on average, closer to the parameter being estimated.. An inefficient estimator is, on average, farther from the parameter being estimated. Efficiency

26. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-26 An estimator is said to be consistent if its probability of being close to the parameter it estimates increases as the sample size increases. An estimator is said to be sufficient if it contains all the information in the data about the parameter it estimates. n = 100 n = 10 Consistency Consistency and Sufficiency

27. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-27 For a normal population, both the sample mean and sample median are unbiased estimators of the population mean, but the sample mean is both more efficient (because it has a smaller variance), and sufficient. Every observation in the sample is used in the calculation of the sample mean, but only the middle value is used to find the sample median. In general, the sample mean is the best estimator of the population mean. The sample mean is the most efficient unbiased estimator of the population mean. It is also a consistent estimator. Properties of the Sample Mean

28. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-28 The sample variance (the sum of the squared deviations from the sample mean divided by (n-1)) is an unbiased estimator of the population variance. In contrast, the average squared deviation from the sample mean is a biased (though consistent) estimator of the population variance. EsE xx n E xx n () () () () 2 2 2 2 2 1                       Properties of the Sample Variance

29. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-29 Consider a sample of size n=4 containing the following data points: x 1 =10x 2 =12x 3 =16x 4 =? and for which the sample mean is: Given the values of three data points and the sample mean, the value of the fourth data point can be determined: 5-5 Degrees of Freedom (1)

30. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-30 If only two data points and the sample mean are known: x 1 =10x 2 =12x 3 =?x 4 =? The values of the remaining two data points cannot be uniquely determined: Degrees of Freedom (2)

31. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-31 The number of degrees of freedom is equal to the total number of measurements (these are not always raw data points), less the total number of restrictions on the measurements. A restriction is a quantity computed from the measurements. The sample mean is a restriction on the sample measurements, so after calculating the sample mean there are only (n-1) degrees of freedom remaining with which to calculate the sample variance. The sample variance is based on only (n-1) free data points: The number of degrees of freedom is equal to the total number of measurements (these are not always raw data points), less the total number of restrictions on the measurements. A restriction is a quantity computed from the measurements. The sample mean is a restriction on the sample measurements, so after calculating the sample mean there are only (n-1) degrees of freedom remaining with which to calculate the sample variance. The sample variance is based on only (n-1) free data points: s xx n 2 2 1     () () Degrees of Freedom (3)

32. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-32 A company manager has a total budget of $150.000 to be completely allocated to four different projects. How many degrees of freedom does the manager have? x 1 + x 2 + x 3 + x 4 = 150,000 A fourth project’s budget can be determined from the total budget and the individual budgets of the other three. For example, if: x 1 =40,000 x 2 =30,000 x 3 =50,000 Then: x 4 =150,000-40,000-30,000-50,000=30,000 So there are (n-1)=3 degrees of freedom. x 1 + x 2 + x 3 + x 4 = 150,000 A fourth project’s budget can be determined from the total budget and the individual budgets of the other three. For example, if: x 1 =40,000 x 2 =30,000 x 3 =50,000 Then: x 4 =150,000-40,000-30,000-50,000=30,000 So there are (n-1)=3 degrees of freedom. Example 5-4

33. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-33 MTB > random 200 c1-c10; SUBC> uniform 0 1. MTB > rmean c1-c10 c11 MTB > histo c11 Character Histogram Histogram of C11 N = 200 Midpoint Count 0.25 1 * 0.30 5 ***** 0.35 11 *********** 0.40 27 *************************** 0.45 36 ************************************ 0.50 40 **************************************** 0.55 34 ********************************** 0.60 21 ********************* 0.65 18 ****************** 0.70 6 ****** 0.75 1 * Constructing a sampling distribution of the mean from a uniform population (n=10): 0.750.700.650.600.550.500.450.400.350.300.25 40 30 20 10 0 C11 F r e q u e n c y Using the Computer

34. COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 5-34 Constructing a sampling distribution of the mean from a uniform population (n=10) using EXCEL (use RANDBETWEEN(0, 1) command to generate values to graph): Histogram of Sample Means 0 50 100 150 200 250 123456789101112131415 Sample Means (Class Midpoints) Frequency CLASS MIDPOINTFREQUENCY 0.150 0.20 0.253 0.326 0.3564 0.4113 0.45183 0.5213 0.55178 0.6128 0.6565 0.720 0.753 0.83 0.850 999 Using the Computer