Applied Statistics and Probability for Engineers
6th Edition
ISBN: 9781118539712
Author: Douglas C. Montgomery
Publisher: WILEY
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Chapter 7.4, Problem 44E
To determine
Obtain the maximum likelihood estimator of p based on a random
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Chapter 7 Solutions
Applied Statistics and Probability for Engineers
Ch. 7.2 - 7-1. Consider the hospital emergency room data...Ch. 7.2 - 7-2. Consider the compressive strength data in...Ch. 7.2 - 7-3. PVC pipe is manufactured with a mean diameter...Ch. 7.2 - 7-4. Suppose that samples of size n = 25 are...Ch. 7.2 - 7-5. A synthetic fiber used in manufacturing...Ch. 7.2 - 7-6. Consider the synthetic fiber in the previous...Ch. 7.2 - 7-7. The compressive strength of concrete is...Ch. 7.2 - 7-8. Consider the concrete specimens in Exercise...Ch. 7.2 - 7-9. A normal population has mean 100 and variance...Ch. 7.2 - 7-10. Suppose that the random variable X has the...
Ch. 7.2 - 7-11. Suppose that X has a discrete uniform...Ch. 7.2 - 7-12. The amount of time that a customer spends...Ch. 7.2 - 7-13. A random sample of size n1 = 16 is selected...Ch. 7.2 - 7-14. A consumer electronics company is comparing...Ch. 7.2 - 7-15. The elasticity of a polymer is affected by...Ch. 7.2 - 7-16. Scientists at the Hopkins Memorial Forest in...Ch. 7.2 - 7-17. From the data in Exercise 6-21 on the pH of...Ch. 7.2 - 7-18. Researchers in the Hopkins Forest (see...Ch. 7.2 - 7-19. Like hurricanes and earthquakes, geomagnetic...Ch. 7.2 - 7-20. Wayne Collier designed an experiment to...Ch. 7.2 - 7-21. Consider a Weibull distribution with shape...Ch. 7.3 - 7-22. A computer software package calculated some...Ch. 7.3 - 7-23. A computer software package calculated some...Ch. 7.3 - 7-24. Let X1 and X2 be independent random...Ch. 7.3 - 7-25. Suppose that we have a random sample X1,...Ch. 7.3 - 7-26. Suppose we have a random sample of size 2n...Ch. 7.3 - 7-27. Let X1 , X2 ,…, X7 denote a random sample...Ch. 7.3 - 7-28. Suppose that and are unbiased estimators...Ch. 7.3 - 7-29. Suppose that and are estimators of the...Ch. 7.3 - 7-30. Suppose that are estimators of θ. We know...Ch. 7.3 - 7-31. Let three random samples of sizes n1 = 20,...Ch. 7.3 - 7-32. (a) Show that is a biased estimator of...Ch. 7.3 - 7-33. Let X1 ,X2, … ,Xn be a random sample of size...Ch. 7.3 - 7-34. Data on pull-off force (pounds) for...Ch. 7.3 - 7-35. Data on the oxide thickness of semiconductor...Ch. 7.3 - 7-36. Suppose that X is the number of observed...Ch. 7.3 - 7-37. and are the sample mean and sample...Ch. 7.3 - 7-38. Two different plasma etchers in a...Ch. 7.3 - 7-39. Of n1 randomly selected engineering students...Ch. 7.4 - 7-44. Let X be a geometric random variable with...Ch. 7.4 - 7-45. Consider the Poisson distribution with...Ch. 7.4 - 7-46. Let X be a random variable with the...Ch. 7.4 - 7-48. Consider the probability density...Ch. 7.4 - 7-49. Let X1, X2, … Xn be uniformly distributed on...Ch. 7.4 - 7-50. Consider the probability density...Ch. 7.4 - 7-51. The Rayleigh distribution has probability...Ch. 7.4 - 7-52. Let X1, X2, …, Xn be uniformly distributed...Ch. 7.4 - 7-53. Consider the Weibull distribution
(a) Find...Ch. 7.4 - 7-55. Suppose that X is a normal random variable...Ch. 7.4 - 7-56. Suppose that X is a normal random variable...Ch. 7.4 - 7-57. Suppose that X is a Poisson random variable...Ch. 7.4 - 7-58. Suppose that X is a normal random variable...Ch. 7.4 - 7-59. The weight of boxes of candy is a normal...Ch. 7.4 - 7-60. The time between failures of a machine has...Ch. 7 - Prob. 61SECh. 7 - 7-62. Suppose that a random variable is normally...Ch. 7 - Prob. 63SECh. 7 - 7-64. A procurement specialist has purchased 25...Ch. 7 - 7-65. A random sample of 36 observations has been...Ch. 7 - Prob. 66SECh. 7 - Prob. 67SECh. 7 - Prob. 68SECh. 7 - 7-69. A manufacturer of semiconductor devices...Ch. 7 - Prob. 70SECh. 7 - Prob. 71SECh. 7 - Prob. 72SECh. 7 - Prob. 73SECh. 7 - 7-74. You plan to use a rod to lay out a square,...Ch. 7 - Prob. 75SECh. 7 - Prob. 76SECh. 7 - Prob. 77SECh. 7 - Prob. 78SECh. 7 - Prob. 79SECh. 7 - Prob. 80SECh. 7 - Prob. 81SECh. 7 - 7-82. Let X be a random variable with mean μ and...Ch. 7 - Prob. 83SECh. 7 - Prob. 84SE
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- please answer these questionsarrow_forwardSelon une économiste d’une société financière, les dépenses moyennes pour « meubles et appareils de maison » ont été moins importantes pour les ménages de la région de Montréal, que celles de la région de Québec. Un échantillon aléatoire de 14 ménages pour la région de Montréal et de 16 ménages pour la région Québec est tiré et donne les données suivantes, en ce qui a trait aux dépenses pour ce secteur d’activité économique. On suppose que les données de chaque population sont distribuées selon une loi normale. Nous sommes intéressé à connaitre si les variances des populations sont égales.a) Faites le test d’hypothèse sur deux variances approprié au seuil de signification de 1 %. Inclure les informations suivantes : i. Hypothèse / Identification des populationsii. Valeur(s) critique(s) de Fiii. Règle de décisioniv. Valeur du rapport Fv. Décision et conclusion b) A partir des résultats obtenus en a), est-ce que l’hypothèse d’égalité des variances pour cette…arrow_forwardAccording to an economist from a financial company, the average expenditures on "furniture and household appliances" have been lower for households in the Montreal area than those in the Quebec region. A random sample of 14 households from the Montreal region and 16 households from the Quebec region was taken, providing the following data regarding expenditures in this economic sector. It is assumed that the data from each population are distributed normally. We are interested in knowing if the variances of the populations are equal. a) Perform the appropriate hypothesis test on two variances at a significance level of 1%. Include the following information: i. Hypothesis / Identification of populations ii. Critical F-value(s) iii. Decision rule iv. F-ratio value v. Decision and conclusion b) Based on the results obtained in a), is the hypothesis of equal variances for this socio-economic characteristic measured in these two populations upheld? c) Based on the results obtained in a),…arrow_forward
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- Question 1 The data shown in Table 1 are and R values for 24 samples of size n = 5 taken from a process producing bearings. The measurements are made on the inside diameter of the bearing, with only the last three decimals recorded (i.e., 34.5 should be 0.50345). Table 1: Bearing Diameter Data Sample Number I R Sample Number I R 1 34.5 3 13 35.4 8 2 34.2 4 14 34.0 6 3 31.6 4 15 37.1 5 4 31.5 4 16 34.9 7 5 35.0 5 17 33.5 4 6 34.1 6 18 31.7 3 7 32.6 4 19 34.0 8 8 33.8 3 20 35.1 9 34.8 7 21 33.7 2 10 33.6 8 22 32.8 1 11 31.9 3 23 33.5 3 12 38.6 9 24 34.2 2 (a) Set up and R charts on this process. Does the process seem to be in statistical control? If necessary, revise the trial control limits. [15 pts] (b) If specifications on this diameter are 0.5030±0.0010, find the percentage of nonconforming bearings pro- duced by this process. Assume that diameter is normally distributed. [10 pts] 1arrow_forward4. (5 pts) Conduct a chi-square contingency test (test of independence) to assess whether there is an association between the behavior of the elderly person (did not stop to talk, did stop to talk) and their likelihood of falling. Below, please state your null and alternative hypotheses, calculate your expected values and write them in the table, compute the test statistic, test the null by comparing your test statistic to the critical value in Table A (p. 713-714) of your textbook and/or estimating the P-value, and provide your conclusions in written form. Make sure to show your work. Did not stop walking to talk Stopped walking to talk Suffered a fall 12 11 Totals 23 Did not suffer a fall | 2 Totals 35 37 14 46 60 Tarrow_forwardQuestion 2 Parts manufactured by an injection molding process are subjected to a compressive strength test. Twenty samples of five parts each are collected, and the compressive strengths (in psi) are shown in Table 2. Table 2: Strength Data for Question 2 Sample Number x1 x2 23 x4 x5 R 1 83.0 2 88.6 78.3 78.8 3 85.7 75.8 84.3 81.2 78.7 75.7 77.0 71.0 84.2 81.0 79.1 7.3 80.2 17.6 75.2 80.4 10.4 4 80.8 74.4 82.5 74.1 75.7 77.5 8.4 5 83.4 78.4 82.6 78.2 78.9 80.3 5.2 File Preview 6 75.3 79.9 87.3 89.7 81.8 82.8 14.5 7 74.5 78.0 80.8 73.4 79.7 77.3 7.4 8 79.2 84.4 81.5 86.0 74.5 81.1 11.4 9 80.5 86.2 76.2 64.1 80.2 81.4 9.9 10 75.7 75.2 71.1 82.1 74.3 75.7 10.9 11 80.0 81.5 78.4 73.8 78.1 78.4 7.7 12 80.6 81.8 79.3 73.8 81.7 79.4 8.0 13 82.7 81.3 79.1 82.0 79.5 80.9 3.6 14 79.2 74.9 78.6 77.7 75.3 77.1 4.3 15 85.5 82.1 82.8 73.4 71.7 79.1 13.8 16 78.8 79.6 80.2 79.1 80.8 79.7 2.0 17 82.1 78.2 18 84.5 76.9 75.5 83.5 81.2 19 79.0 77.8 20 84.5 73.1 78.2 82.1 79.2 81.1 7.6 81.2 84.4 81.6 80.8…arrow_forward
- Name: Lab Time: Quiz 7 & 8 (Take Home) - due Wednesday, Feb. 26 Contingency Analysis (Ch. 9) In lab 5, part 3, you will create a mosaic plot and conducted a chi-square contingency test to evaluate whether elderly patients who did not stop walking to talk (vs. those who did stop) were more likely to suffer a fall in the next six months. I have tabulated the data below. Answer the questions below. Please show your calculations on this or a separate sheet. Did not stop walking to talk Stopped walking to talk Totals Suffered a fall Did not suffer a fall Totals 12 11 23 2 35 37 14 14 46 60 Quiz 7: 1. (2 pts) Compute the odds of falling for each group. Compute the odds ratio for those who did not stop walking vs. those who did stop walking. Interpret your result verbally.arrow_forwardSolve please and thank you!arrow_forward7. In a 2011 article, M. Radelet and G. Pierce reported a logistic prediction equation for the death penalty verdicts in North Carolina. Let Y denote whether a subject convicted of murder received the death penalty (1=yes), for the defendant's race h (h1, black; h = 2, white), victim's race i (i = 1, black; i = 2, white), and number of additional factors j (j = 0, 1, 2). For the model logit[P(Y = 1)] = a + ß₁₂ + By + B²², they reported = -5.26, D â BD = 0, BD = 0.17, BY = 0, BY = 0.91, B = 0, B = 2.02, B = 3.98. (a) Estimate the probability of receiving the death penalty for the group most likely to receive it. [4 pts] (b) If, instead, parameters used constraints 3D = BY = 35 = 0, report the esti- mates. [3 pts] h (c) If, instead, parameters used constraints Σ₁ = Σ₁ BY = Σ; B = 0, report the estimates. [3 pts] Hint the probabilities, odds and odds ratios do not change with constraints.arrow_forward
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