Applied Statistics and Probability for Engineers
Applied Statistics and Probability for Engineers
6th Edition
ISBN: 9781118539712
Author: Douglas C. Montgomery
Publisher: WILEY
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Chapter 15, Problem 139SE

a.

To determine

Verify that the given control limits are correct.

b.

To determine

Apply C chart to the data given in Example 15-5.

c.

To determine

Identify whether the C chart gives an equivalent result as U chart.

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Problem 3. Pricing a multi-stock option the Margrabe formula The purpose of this problem is to price a swap option in a 2-stock model, similarly as what we did in the example in the lectures. We consider a two-dimensional Brownian motion given by W₁ = (W(¹), W(2)) on a probability space (Q, F,P). Two stock prices are modeled by the following equations: dX = dY₁ = X₁ (rdt+ rdt+0₁dW!) (²)), Y₁ (rdt+dW+0zdW!"), with Xo xo and Yo =yo. This corresponds to the multi-stock model studied in class, but with notation (X+, Y₁) instead of (S(1), S(2)). Given the model above, the measure P is already the risk-neutral measure (Both stocks have rate of return r). We write σ = 0₁+0%. We consider a swap option, which gives you the right, at time T, to exchange one share of X for one share of Y. That is, the option has payoff F=(Yr-XT). (a) We first assume that r = 0 (for questions (a)-(f)). Write an explicit expression for the process Xt. Reminder before proceeding to question (b): Girsanov's theorem…
Problem 1. Multi-stock model We consider a 2-stock model similar to the one studied in class. Namely, we consider = S(1) S(2) = S(¹) exp (σ1B(1) + (M1 - 0/1 ) S(²) exp (02B(2) + (H₂- M2 where (B(¹) ) +20 and (B(2) ) +≥o are two Brownian motions, with t≥0 Cov (B(¹), B(2)) = p min{t, s}. " The purpose of this problem is to prove that there indeed exists a 2-dimensional Brownian motion (W+)+20 (W(1), W(2))+20 such that = S(1) S(2) = = S(¹) exp (011W(¹) + (μ₁ - 01/1) t) 롱) S(²) exp (021W (1) + 022W(2) + (112 - 03/01/12) t). where σ11, 21, 22 are constants to be determined (as functions of σ1, σ2, p). Hint: The constants will follow the formulas developed in the lectures. (a) To show existence of (Ŵ+), first write the expression for both W. (¹) and W (2) functions of (B(1), B(²)). as (b) Using the formulas obtained in (a), show that the process (WA) is actually a 2- dimensional standard Brownian motion (i.e. show that each component is normal, with mean 0, variance t, and that their…
The scores of 8 students on the midterm exam and final exam were as follows.   Student Midterm Final Anderson 98 89 Bailey 88 74 Cruz 87 97 DeSana 85 79 Erickson 85 94 Francis 83 71 Gray 74 98 Harris 70 91   Find the value of the (Spearman's) rank correlation coefficient test statistic that would be used to test the claim of no correlation between midterm score and final exam score. Round your answer to 3 places after the decimal point, if necessary. Test statistic: rs =

Chapter 15 Solutions

Applied Statistics and Probability for Engineers

Ch. 15.3 - 15-11. The thickness of a metal part is an...Ch. 15.3 - 15-12. Apply the Western Electric Rules to the...Ch. 15.3 - 15-13. Apply the Western Electric Rules to the...Ch. 15.3 - 15-14. Web traffic can be measured to help...Ch. 15.3 - 15-15. Consider the data in Exercise 15-9....Ch. 15.3 - 15-16. Consider the data in Exercise 15-10....Ch. 15.3 - 15-17. An X control chart with 3-sigma control...Ch. 15.3 - 15-18. An article in Quality & Safety in Health...Ch. 15.4 - 15-19. Twenty successive hardness measurements are...Ch. 15.4 - 15-20. In a semiconductor manufacturing process,...Ch. 15.4 - 15-21. O An automatic sensor measures the diameter...Ch. 15.4 - 15-22. The viscosity of a chemical intermediate is...Ch. 15.4 - 15-23. The following table of data was analyzed in...Ch. 15.4 - 15-24. Pulsed laser deposition technique is a thin...Ch. 15.4 - 15-25. The production manager of a soap...Ch. 15.4 - 15-26. An article in Quality & Safety in Health...Ch. 15.4 - 15-27. An article in Journal of the Operational...Ch. 15.5 - 15-28. Suppose that a quality characteristic is...Ch. 15.5 - 15-29. Suppose that a quality characteristic is...Ch. 15.5 - 15-30. Suppose that a quality characteristic is...Ch. 15.5 - 15-31. A normally distributed process uses 66.7%...Ch. 15.5 - 15-32. A normally distributed process uses 85% of...Ch. 15.5 - 15-33. Reconsider Exercise 15-1. Suppose that the...Ch. 15.5 - 15-34. Reconsider Exercise 15-2 in which the...Ch. 15.5 - 15-35. Reconsider Exercise 15-3. Suppose that the...Ch. 15.5 - 15-36. Reconsider Exercise 15-4(a). Assuming that...Ch. 15.5 - 15-37. Reconsider the diameter measurements in...Ch. 15.5 - 15-38. Reconsider the copper-content measurements...Ch. 15.5 - 15-39. Reconsider the pull-strength measurements...Ch. 15.5 - 15-40. Reconsider the syringe lengths in Exercise...Ch. 15.5 - 15-41. Reconsider the hardness measurements in...Ch. 15.5 - 15-42. Reconsider the viscosity measurements in...Ch. 15.5 - 15-43. Suppose that a quality characteristic is...Ch. 15.5 - 15-44. Suppose that a quality characteristic is...Ch. 15.5 - 15-45. An control chart with 3-sigma control...Ch. 15.5 - 15-46. A control chart for individual observations...Ch. 15.5 - 15-47. A process mean is centered between the...Ch. 15.5 - 15-48. The PCR for a measurement is 1.5 and the...Ch. 15.6 - 15-49. An early example of SPC was described in...Ch. 15.6 - 15-50. Suppose that the following fraction...Ch. 15.6 - 15-51. The following are the numbers of defective...Ch. 15.6 - 15-52. The following represent the number of...Ch. 15.6 - 15-53. The following represent the number of...Ch. 15.6 - 15-54. Consider the data on the number of...Ch. 15.6 - 15-55. In a semiconductor manufacturing company,...Ch. 15.6 - Prob. 56ECh. 15.6 - Prob. 57ECh. 15.6 - Prob. 58ECh. 15.7 - Prob. 59ECh. 15.7 - Prob. 60ECh. 15.7 - 15-61. Consider the control chart in Fig. 15-3....Ch. 15.7 - Prob. 62ECh. 15.7 - Prob. 63ECh. 15.7 - Prob. 64ECh. 15.7 - Prob. 65ECh. 15.7 - Prob. 66ECh. 15.7 - Prob. 67ECh. 15.7 - Prob. 68ECh. 15.7 - Prob. 69ECh. 15.7 - 15-70. Consider an control chart with UCL =...Ch. 15.7 - Prob. 71ECh. 15.7 - Prob. 72ECh. 15.8 - Prob. 73ECh. 15.8 - Prob. 74ECh. 15.8 - Prob. 75ECh. 15.8 - Prob. 76ECh. 15.8 - Prob. 77ECh. 15.8 - Prob. 78ECh. 15.8 - Prob. 79ECh. 15.8 - Prob. 80ECh. 15.8 - Prob. 81ECh. 15.8 - 15-82. A process has a target of μ0 = 100 and a...Ch. 15.8 - 15-83. Heart rate (in counts/minute) is measured...Ch. 15.8 - Prob. 84ECh. 15.8 - Prob. 85ECh. 15.8 - Prob. 86ECh. 15.9 - Prob. 87ECh. 15.9 - Prob. 88ECh. 15.9 - Prob. 89ECh. 15.9 - Prob. 90ECh. 15 - Prob. 91SECh. 15 - 15-92. Rework Exercise 15-91 with and S...Ch. 15 - Prob. 93SECh. 15 - Prob. 94SECh. 15 - 15-95. An article in Quality Engineering [“Is the...Ch. 15 - Prob. 96SECh. 15 - Prob. 97SECh. 15 - Prob. 98SECh. 15 - Prob. 99SECh. 15 - Prob. 100SECh. 15 - Prob. 101SECh. 15 - Prob. 102SECh. 15 - Prob. 103SECh. 15 - Prob. 104SECh. 15 - Prob. 105SECh. 15 - Prob. 106SECh. 15 - Prob. 107SECh. 15 - Prob. 108SECh. 15 - 15-109. The depth of a keyway is an important part...Ch. 15 - Prob. 110SECh. 15 - Prob. 111SECh. 15 - Prob. 112SECh. 15 - Prob. 113SECh. 15 - Prob. 114SECh. 15 - Prob. 115SECh. 15 - Prob. 117SECh. 15 - Prob. 118SECh. 15 - 15-119. Consider an control chart with UCL =...Ch. 15 - Prob. 120SECh. 15 - Prob. 121SECh. 15 - Prob. 122SECh. 15 - Prob. 123SECh. 15 - Prob. 124SECh. 15 - Prob. 125SECh. 15 - Prob. 126SECh. 15 - Prob. 127SECh. 15 - Prob. 128SECh. 15 - Prob. 129SECh. 15 - Prob. 130SECh. 15 - Prob. 131SECh. 15 - 15-132. Consider an control chart with k-sigma...Ch. 15 - Prob. 133SECh. 15 - Prob. 134SECh. 15 - Prob. 135SECh. 15 - Prob. 136SECh. 15 - 15-137. Consider a process whose specifications on...Ch. 15 - Prob. 138SECh. 15 - Prob. 139SECh. 15 - Prob. 140SECh. 15 - Prob. 141SE
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