1. 1 Pertemuan 06 Sebaran Normal dan Sampling Matakuliah: >K0614/ >FISIKA Tahun: >2006

2. 2 Outline Materi: Peluang sebaran normal Sebaran rata-rata sampling Sebaran proporsi sampling

3. 3 Basic Business Statistics (9 th Edition) The Normal Distribution and Other Continuous Distributions

4. 4 Peluang sebaran normal The Normal Distribution The Standardized Normal Distribution Evaluating the Normality Assumption The Uniform Distribution The Exponential Distribution

5. 5 Continuous Probability Distributions Continuous Random Variable –Values from interval of numbers –Absence of gaps Continuous Probability Distribution –Distribution of continuous random variable Most Important Continuous Probability Distribution –The normal distribution

6. 6 The Normal Distribution “Bell Shaped” Symmetrical Mean, Median and Mode are Equal Interquartile Range Equals 1.33  Random Variable Has Infinite Range Mean Median Mode X f(X) 

7. 7 The Mathematical Model

8. 8 Many Normal Distributions Varying the Parameters  and , We Obtain Different Normal Distributions There are an Infinite Number of Normal Distributions

9. 9 The Standardized Normal Distribution When X is normally distributed with a mean and a standard deviation, follows a standardized (normalized) normal distribution with a mean 0 and a standard deviation 1. X f(X)f(X) f(Z)f(Z)

10. 10 Finding Probabilities Probability is the area under the curve! c d X f(X)f(X)

11. 11 Which Table to Use? Infinitely Many Normal Distributions Means Infinitely Many Tables to Look Up!

12. 12 Solution: The Cumulative Standardized Normal Distribution Z.00.01 0.0.5000.5040.5080.5398.5438 0.2.5793.5832.5871 0.3.6179.6217.6255.5478.02 0.1. 5478 Cumulative Standardized Normal Distribution Table (Portion) Probabilities Only One Table is Needed Z = 0.12

13. 13 Standardizing Example Normal Distribution Standardized Normal Distribution

14. 14 Example Normal Distribution Standardized Normal Distribution

15. 15 Z.00.01 0.0.5000.5040.5080.5398.5438 0.2.5793.5832.5871 0.3.6179.6217.6255.5832.02 0.1. 5478 Cumulative Standardized Normal Distribution Table (Portion) Z = 0.21 Example (continued)

16. 16 Z.00.01 -0.3.3821.3783.3745.4207.4168 -0.1.4602.4562.4522 0.0.5000.4960.4920.4168.02 -0.2.4129 Cumulative Standardized Normal Distribution Table (Portion) Z = -0.21 Example (continued)

17. 17 Normal Distribution in PHStat PHStat | Probability & Prob. Distributions | Normal … Example in Excel Spreadsheet

18. 18 Example : Normal Distribution Standardized Normal Distribution

19. 19 Example: (continued) Z.00.01 0.0.5000.5040.5080.5398.5438 0.2.5793.5832.5871 0.3.6179.6217.6255.6179.02 0.1. 5478 Cumulative Standardized Normal Distribution Table (Portion) Z = 0.30

20. 20.6217 Finding Z Values for Known Probabilities Z.000.2 0.0.5000.5040.5080 0.1.5398.5438.5478 0.2.5793.5832.5871.6179.6255.01 0.3 Cumulative Standardized Normal Distribution Table (Portion) What is Z Given Probability = 0.6217 ?.6217

21. 21 Recovering X Values for Known Probabilities Normal Distribution Standardized Normal Distribution

22. 22 More Examples of Normal Distribution Using PHStat A set of final exam grades was found to be normally distributed with a mean of 73 and a standard deviation of 8. What is the probability of getting a grade no higher than 91 on this exam? 2.25 91

23. 23 What percentage of students scored between 65 and 89? 2 8965 More Examples of Normal Distribution Using PHStat (continued)

24. 24 Only 5% of the students taking the test scored higher than what grade? 1.645 ? =86.16 (continued) More Examples of Normal Distribution Using PHStat

25. 25 Assessing Normality Not All Continuous Random Variables are Normally Distributed It is Important to Evaluate How Well the Data Set Seems to Be Adequately Approximated by a Normal Distribution

26. 26 Assessing Normality Construct Charts –For small- or moderate-sized data sets, do the stem-and-leaf display and box-and- whisker plot look symmetric? –For large data sets, does the histogram or polygon appear bell-shaped? Compute Descriptive Summary Measures –Do the mean, median and mode have similar values? –Is the interquartile range approximately 1.33  ? –Is the range approximately 6  ? (continued)

27. 27 Assessing Normality Observe the Distribution of the Data Set –Do approximately 2/3 of the observations lie between mean 1 standard deviation? –Do approximately 4/5 of the observations lie between mean 1.28 standard deviations? –Do approximately 19/20 of the observations lie between mean 2 standard deviations? Evaluate Normal Probability Plot –Do the points lie on or close to a straight line with positive slope? (continued)

28. 28 Assessing Normality Normal Probability Plot –Arrange Data into Ordered Array –Find Corresponding Standardized Normal Quantile Values –Plot the Pairs of Points with Observed Data Values on the Vertical Axis and the Standardized Normal Quantile Values on the Horizontal Axis –Evaluate the Plot for Evidence of Linearity (continued)

29. 29 Assessing Normality Normal Probability Plot for Normal Distribution Look for Straight Line! 30 60 90 -2012 Z X (continued)

30. 30 Normal Probability Plot Left-SkewedRight-Skewed RectangularU-Shaped 30 60 90 -2012 Z X 30 60 90 -2012 Z X 30 60 90 -2012 Z X 30 60 90 -2012 Z X

31. 31 Sampling Distribution Sampling Distribution of the Mean The Central Limit Theorem Sampling Distribution of the Proportion Sampling from Finite Population

32. 32 Why Study Sampling Distributions Sample Statistics are Used to Estimate Population Parameters –E.g., estimates the population mean Problem: Different Samples Provide Different Estimates –Large sample gives better estimate; large sample costs more –How good is the estimate? Approach to Solution: Theoretical Basis is Sampling Distribution

33. 33 Sampling Distribution Theoretical Probability Distribution of a Sample Statistic Sample Statistic is a Random Variable –Sample mean, sample proportion Results from Taking All Possible Samples of the Same Size

34. 34 Developing Sampling Distributions Suppose There is a Population … Population Size N=4 Random Variable, X, is Age of Individuals Values of X: 18, 20, 22, 24 Measured in Years A B C D

35. 35.3.2.1 0 A B C D (18) (20) (22) (24) Uniform Distribution P(X) X Developing Sampling Distributions (continued) Summary Measures for the Population Distribution

36. 36 All Possible Samples of Size n=2 16 Samples Taken with Replacement 16 Sample Means Developing Sampling Distributions (continued)

37. 37 Sampling Distribution of All Sample Means 18 19 20 21 22 23 24 0.1.2.3 X Sample Means Distribution 16 Sample Means _ Developing Sampling Distributions (continued)

38. 38 Summary Measures of Sampling Distribution Developing Sampling Distributions (continued)

39. 39 Comparing the Population with Its Sampling Distribution 18 19 20 21 22 23 24 0.1.2.3 X Sample Means Distribution n = 2 A B C D (18) (20) (22) (24) 0.1.2.3 Population N = 4 X _

40. 40 Properties of Summary Measures –I.e., is unbiased Standard Error (Standard Deviation) of the Sampling Distribution is Less Than the Standard Error of Other Unbiased Estimators For Sampling with Replacement or without Replacement from Large or Infinite Populations: –As n increases, decreases

41. 41 Unbiasedness ( ) BiasedUnbiased

42. 42 Less Variability Sampling Distribution of Median Sampling Distribution of Mean Standard Error (Standard Deviation) of the Sampling Distribution is Less Than the Standard Error of Other Unbiased Estimators

43. 43 Effect of Large Sample Larger sample size Smaller sample size

44. 44 When the Population is Normal Central Tendency Variation Population Distribution Sampling Distributions

45. 45 When the Population is Not Normal Central Tendency Variation Population Distribution Sampling Distributions

46. 46 Central Limit Theorem As Sample Size Gets Large Enough Sampling Distribution Becomes Almost Normal Regardless of Shape of Population

47. 47 How Large is Large Enough? For Most Distributions, n>30 For Fairly Symmetric Distributions, n>15 For Normal Distribution, the Sampling Distribution of the Mean is Always Normally Distributed Regardless of the Sample Size –This is a property of sampling from a normal population distribution and is NOT a result of the central limit theorem

48. 48 Example: Sampling Distribution Standardized Normal Distribution

49. 49 Population Proportions Categorical Variable –E.g., Gender, Voted for Bush, College Degree Proportion of Population Having a Characteristic Sample Proportion Provides an Estimate – If Two Outcomes, X Has a Binomial Distribution –Possess or do not possess characteristic

50. 50 Sampling Distribution of Sample Proportion Approximated by Normal Distribution – –Mean: –Standard error: p = population proportion Sampling Distribution f(p s ).3.2.1 0 0. 2.4.6 8 1 psps

51. 51 Standardizing Sampling Distribution of Proportion Sampling Distribution Standardized Normal Distribution

52. 52 Example: Sampling Distribution Standardized Normal Distribution