Applied Statistics and Probability for Engineers
6th Edition
ISBN: 9781118539712
Author: Douglas C. Montgomery
Publisher: WILEY
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Chapter 12.1, Problem 1E
a.
To determine
Find the regression model from the given matrices of
b.
To determine
Verify whether the model obtained from technology is correct up to at least 2 decimal places.
c.
To determine
Find the body weight of a 6-ft tall man with a 34-in waist.
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A major company in the Montreal area, offering a range of engineering services from project preparation to construction execution, and industrial project management, wants to ensure that the individuals who are responsible for project cost estimation and bid preparation demonstrate a certain uniformity in their estimates. The head of civil engineering and municipal services decided to structure an experimental plan to detect if there could be significant differences in project evaluation.
Seven projects were selected, each of which had to be evaluated by each of the two estimators, with the order of the projects submitted being random. The obtained estimates are presented in the table below.
a) Complete the table above by calculating: i. The differences (A-B) ii. The sum of the differences iii. The mean of the differences iv. The standard deviation of the differences
b) What is the value of the t-statistic?
c) What is the critical t-value for this test at a significance level of 1%?…
Compute the relative risk of falling for the two groups (did not stop walking vs. did stop). State/interpret your result verbally.
Microsoft Excel include formulas
Chapter 12 Solutions
Applied Statistics and Probability for Engineers
Ch. 12.1 - 12.1. Exercise 11.1 described a regression model...Ch. 12.1 - 12.2. A class of 63 students has two hourly exams...Ch. 12.1 - 12.3. Can the percentage of the workforce who are...Ch. 12.1 - Prob. 4ECh. 12.1 - Prob. 5ECh. 12.1 - Prob. 6ECh. 12.1 - Prob. 7ECh. 12.1 - 12-8. You have fit a multiple linear regression...Ch. 12.1 - 12-9. The data from a patient satisfaction survey...Ch. 12.1 - 12-10. The electric power consumed each month by a...
Ch. 12.1 - 12-11. Table E12-3 provides the highway gasoline...Ch. 12.1 - 12-12. The pull strength of a wire bond is an...Ch. 12.1 - Prob. 13ECh. 12.1 - Prob. 14ECh. 12.1 - 12-15. An article in Electronic Packaging and...Ch. 12.1 - 12-16. An article in Cancer Epidemiology,...Ch. 12.1 - Prob. 17ECh. 12.1 - Prob. 18ECh. 12.1 - Prob. 19ECh. 12.1 - Prob. 20ECh. 12.1 - Prob. 21ECh. 12.1 - Prob. 22ECh. 12.1 - 12-23. A study was performed on wear of a bearing...Ch. 12.1 - Prob. 24ECh. 12.2 - 12-25. Recall the regression of percent of body...Ch. 12.2 - Prob. 27ECh. 12.2 - Prob. 28ECh. 12.2 - 12-29. Consider the following computer...Ch. 12.2 - 12-30. You have fit a regression model with two...Ch. 12.2 - 12-31. Consider the regression model fit to the...Ch. 12.2 - 12-32. Consider the absorption index data in...Ch. 12.2 - Prob. 33ECh. 12.2 - Prob. 34ECh. 12.2 - 12-35. Consider the gasoline mileage data in...Ch. 12.2 - Prob. 36ECh. 12.2 - Prob. 37ECh. 12.2 - Prob. 38ECh. 12.2 - 12-39. Consider the regression model fit to the...Ch. 12.2 - Prob. 40ECh. 12.2 - Prob. 41ECh. 12.2 - Prob. 42ECh. 12.2 - 12-43. Consider the NFL data in Exercise...Ch. 12.2 - Prob. 44ECh. 12.2 - 12-45. Consider the bearing wear data in Exercise...Ch. 12.2 - 12-46. Data on National Hockey League team...Ch. 12.2 - Prob. 47ECh. 12.2 - Prob. 48ECh. 12.4 - Prob. 52ECh. 12.4 - 12-53. Consider the regression model fit to the...Ch. 12.4 - 12-55. Consider the semiconductor data in Exercise...Ch. 12.4 - 12-56. Consider the electric power consumption...Ch. 12.4 - Prob. 57ECh. 12.4 - Prob. 58ECh. 12.4 - 12-59. Consider the regression model fit to the...Ch. 12.4 - Prob. 60ECh. 12.4 - 12-61. Consider the regression model fit to the...Ch. 12.4 - Prob. 62ECh. 12.4 - Prob. 63ECh. 12.4 - Prob. 64ECh. 12.4 - Prob. 65ECh. 12.4 - Prob. 66ECh. 12.4 - Prob. 67ECh. 12.4 - 12-68. Consider the NHL data in Exercise...Ch. 12.5 - 12-69. Consider the gasoline mileage data in...Ch. 12.5 - Prob. 70ECh. 12.5 - Prob. 71ECh. 12.5 - Prob. 72ECh. 12.5 - 12-73. Consider the regression model fit to the...Ch. 12.5 - Prob. 74ECh. 12.5 - Prob. 75ECh. 12.5 - Prob. 76ECh. 12.5 - Prob. 77ECh. 12.5 - Prob. 78ECh. 12.5 - Prob. 79ECh. 12.5 - 12-80. Fit a model to the response PITCH in the...Ch. 12.5 - Prob. 81ECh. 12.6 - 12-84. An article entitled “A Method for Improving...Ch. 12.6 - Prob. 85ECh. 12.6 - Prob. 86ECh. 12.6 - Prob. 87ECh. 12.6 - 12-88. Consider the arsenic concentration data in...Ch. 12.6 - Prob. 89ECh. 12.6 - Prob. 90ECh. 12.6 - 12-91. Consider the X-ray inspection data in...Ch. 12.6 - 12-92. Consider the electric power data in...Ch. 12.6 - Prob. 93ECh. 12.6 - Prob. 94ECh. 12.6 - 12-95. Consider the gray range modulation data in...Ch. 12.6 - 12-96. Consider the nisin extraction data in...Ch. 12.6 - Prob. 97ECh. 12.6 - Prob. 98ECh. 12.6 - Prob. 99ECh. 12.6 - 12-100. Consider the arsenic data in Exercise...Ch. 12.6 - 12-101. Consider the gas mileage data in Exercise...Ch. 12.6 - Prob. 102ECh. 12.6 - Prob. 103ECh. 12.6 - Prob. 104ECh. 12.6 - Prob. 105ECh. 12 - Prob. 106SECh. 12 - 12-107. Consider the following inverse of the...Ch. 12 - 12-108. The data shown in Table E12-14 represent...Ch. 12 - Prob. 109SECh. 12 - Prob. 111SECh. 12 - Prob. 112SECh. 12 - 12-113. Consider the jet engine thrust data in...Ch. 12 - 12-114. Consider the electronic inverter data in...Ch. 12 - 12-115. A multiple regression model was used to...Ch. 12 - Prob. 116SECh. 12 - 12-117. An article in the Journal of the American...Ch. 12 - 12-118. Exercise 12-9 introduced the hospital...Ch. 12 - Prob. 119SECh. 12 - Prob. 120SECh. 12 - 12-121. A regression model is used to relate a...Ch. 12 - Prob. 122SECh. 12 - Prob. 123SECh. 12 - Prob. 124SECh. 12 - Prob. 125SE
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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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