4. 3-2 Random Variables In an experiment, a measurement is usually denoted by a variable such as X. In a random experiment, a variable whose measured value can change (from one replicate of the experiment to another) is referred to as a random variable.

5. 3-2 Random Variables

6. 3-3 Probability Used to quantify likelihood or chance Used to represent risk or uncertainty in engineering applications Can be interpreted as our degree of belief or relative frequency

7. 3-3 Probability Probability statements describe the likelihood that particular values occur. The likelihood is quantified by assigning a number from the interval [0, 1] to the set of values (or a percentage from 0 to 100%). Higher numbers indicate that the set of values is more likely.

8. 3-3 Probability A probability is usually expressed in terms of a random variable. For the part length example, X denotes the part length and the probability statement can be written in either of the following forms Both equations state that the probability that the random variable X assumes a value in [10.8, 11.2] is 0.25.

9. 3-3 Probability Complement of an Event Given a set E, the complement of E is the set of elements that are not in E. The complement is denoted as E ’. Mutually Exclusive Events The sets E 1, E 2,...,E k are mutually exclusive if the intersection of any pair is empty. That is, each element is in one and only one of the sets E 1, E 2,...,E k.

10. 3-3 Probability Probability Properties

11. 3-3 Probability Events A measured value is not always obtained from an experiment. Sometimes, the result is only classified (into one of several possible categories). These categories are often referred to as events. Illustrations The current measurement might only be recorded as low, medium, or high; a manufactured electronic component might be classified only as defective or not; and either a message is sent through a network or not.

12. 3-4 Continuous Random Variables 3-4.1 Probability Density Function

13. 3-4 Continuous Random Variables 3-4.1 Probability Density Function The probability distribution or simply distribution of a random variable X is a description of the set of the probabilities associated with the possible values for X.

14. 3-4 Continuous Random Variables 3-4.1 Probability Density Function

15. 3-4 Continuous Random Variables 3-4.1 Probability Density Function

16. 3-4 Continuous Random Variables

18. 3-4.2 Cumulative Distribution Function

19. 3-4 Continuous Random Variables

22. 3-4.3 Mean and Variance

23. 3-4 Continuous Random Variables

24. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution Undoubtedly, the most widely used model for the distribution of a random variable is a normal distribution. Central limit theorem Gaussian distribution

25. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution

26. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution

27. 3-5 Important Continuous Distributions

28. 3-5.1 Normal Distribution

29. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution

30. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution

31. 3-5 Important Continuous Distributions

32. 3-5.1 Normal Distribution

33. 3-5 Important Continuous Distributions 3-5.1 Normal Distribution

34. 3-5 Important Continuous Distributions

36. OPTIONS NOPAGE NODATE LS=80; DATA PAGE82; MEAN=10; SD=2;X=13;P=0.98; Z1=(X-MEAN)/SD; P1=PROBNORM(Z1); Z2=PROBIT(P); X1=MEAN+Z2*SD; PROC PRINT; VAR X P1; VAR P X1; TITLE 'EXAMPLE IN PAGE 82-83'; RUN; QUIT; EXAMPLE IN PAGE 82-83 OBS X P1 P X1 1 13 0.93319 0.98 14.1075

37. 3-5 Important Continuous Distributions DATA P342B; Z=PROBIT(0.05); /The PROBIT function returns the p th quantile from the standard normal distribution. The probability that an observation from the standard normal distribution is less than or equal to the returned quantile is p./ MU=20; STD=2; X= MU+Z*STD; PROC PRINT; VAR X Z; TITLE 'PROB 3-42 (B) IN PAGE 90'; DATA P343D; MU=27; SIGMA=2;XU=29;XL=22; ZU=(XU-MU)/SIGMA; ZL=(XL-MU)/SIGMA; P1=PROBNORM(ZU); P2=PROBNORM(ZL); /The PROBNORM function returns the probability that an observation from the standard normal distribution is less than or equal to x./ ANS=P1-P2; PROC PRINT; VAR ZU ZL P1 P2 ANS; TITLE 'PROB 3-43 (D) IN PAGE 90'; RUN; QUIT; PROB 3-42 (B) IN PAGE 90 OBS X Z 1 16.7103 -1.64485 PROB 3-43 (D) IN PAGE 90 OBS ZU ZL P1 P2 ANS 1 1 -2.5 0.84134.006209665 0.83514

38. 3-6 Probability Plots 3-6.1 Normal Probability Plots How do we know if a normal distribution is a reasonable model for data? Probability plotting is a graphical method for determining whether sample data conform to a hypothesized distribution based on a subjective visual examination of the data. Probability plotting typically uses special graph paper, known as probability paper, that has been designed for the hypothesized distribution. Probability paper is widely available for the normal, lognormal, Weibull, and various chi- square and gamma distributions.

39. 3-6 Probability Plots 3-6.1 Normal Probability Plots

40. 3-6 Probability Plots 3-6.1 Normal Probability Plots

41. 3-7 Discrete Random Variables Only measurements at discrete points are possible

42. 3-7 Discrete Random Variables 3-7.1 Probability Mass Function

43. 3-7 Discrete Random Variables 3-7.1 Probability Mass Function

44. 3-7 Discrete Random Variables 3-7.2 Cumulative Distribution Function

45. 3-7 Discrete Random Variables 3-7.2 Cumulative Distribution Function

46. 3-7 Discrete Random Variables 3-7.3 Mean and Variance

47. 3-7 Discrete Random Variables 3-7.3 Mean and Variance

48. 3-7 Discrete Random Variables 3-7.3 Mean and Variance

49. 3-11 More Than One Random Variable and Independence 3-11.1 Joint Distributions

50. 3-11 More Than One Random Variable and Independence 3-11.1 Joint Distributions

51. 3-11 More Than One Random Variable and Independence 3-11.1 Joint Distributions

52. 3-11 More Than One Random Variable and Independence 3-11.1 Joint Distributions

53. 3-11 More Than One Random Variable and Independence 3-11.2 Independence

54. 3-11 More Than One Random Variable and Independence 3-11.2 Independence

55. 3-11 More Than One Random Variable and Independence 3-11.2 Independence

56. 3-12 Functions of Random Variables

57. 3-12.1 Linear Combinations of Independent Random Variables

58. 3-12 Functions of Random Variables 3-12.1 Linear Combinations of Independent Random Variables

59. 3-12 Functions of Random Variables 3-12.1 Linear Combinations of Independent Random Variables

60. 3-12 Functions of Random Variables 3-12.2 What If the Random Variables Are Not Independent?

61. 3-12 Functions of Random Variables 3-12.2 What If the Random Variables Are Not Independent?

62. 3-13 Random Samples, Statistics, and The Central Limit Theorem

63. 3-13 Random Samples, Statistics, and The Central Limit Theorem Central Limit Theorem

64. 3-13 Random Samples, Statistics, and The Central Limit Theorem

65. 3-13 Random Samples, Statistics, and The Central Limit Theorem

66. 3-13 Random Samples, Statistics, and The Central Limit Theorem

67. 3-13 Random Samples, Statistics, and The Central Limit Theorem OPTIONS NODATE NONUMBER; DATAEX3195AD; MU=200; SD=9; N=16; SDBAR=SD/SQRT(N); ZU=(202-MU)/SDBAR; ZL=(196-MU)/SDBAR; P1=PROBNORM(ZU); P2=PROBNORM(ZL); ANS=P1-P2; PROC PRINT; VAR MU SDBAR ZL ZU P1 P2 ANS; TITLE 'PROB 3-195 IN PAGE 140’; RUN; QUIT; PROB 3-195 IN PAGE 140 OBS MU SDBAR ZL ZU P1 P2 ANS 1 200 2.25 -1.77778 0.88889 0.81297 0.037720 0.77525