MindTap Business Statistics, 1 term (6 months) Printed Access Card for Anderson/Sweeney/Williams/Camm/Cochran's Essentials of Statistics for Business and Economics, 8th
8th Edition
ISBN: 9781337114288
Author: Anderson, David R.; Sweeney, Dennis J.; Williams, Thomas A.; Camm, Jeffrey D.; Cochran, James J.
Publisher: Cengage Learning
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Textbook Question
Chapter 6, Problem 47SE
According to the National Association of Colleges and Employers, the average starting salary for new college graduates in health sciences is $51,541. The average starting salary for new college graduates in business is $53,901 (National Association of Colleges and Employers website, January 5, 2015). Assume that starting salaries are
- a. What is the
probability that a new college graduate in business will earn a starting salary of at least $65,000? - b. What is the probability that a new college graduate in health sciences will earn a starting salary of at least $65,000?
- c. What is the probability that a new college graduate in health sciences will earn a starting salary less than $40,000?
- d. How much would a new college graduate in business have to earn in order to have a starting salary higher than 99% of all starting salaries of new college graduates in the health sciences?
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Question 2
A nickel-titanium alloy is used to make components for jet turbine aircraft engines. Cracking is a potentially
serious problem in the final part because it can lead to nonrecoverable failure. A test is run at the parts producer
to determine the effect of four factors on cracks. The four factors are: pouring temperature (A), titanium content
(B), heat treatment method (C), amount of grain refiner used (D). Two replicates of a 24 design are run, and
the length of crack (in mm x10-2) induced in a sample coupon subjected to a standard test is measured. The
data are shown in Table 2.
1
(a) Estimate the factor effects. Which factor effects appear to be large?
(b) Conduct an analysis of variance. Do any of the factors affect cracking? Use a = 0.05.
(c) Write down a regression model that can be used to predict crack length as a function of the significant
main effects and interactions you have identified in part (b).
(d) Analyze the residuals from this experiment.
(e) Is there an…
A 24-1 design has been used to investigate the effect of four factors on the resistivity of a silicon wafer. The data
from this experiment are shown in Table 4.
Table 4: Resistivity Experiment for Exercise 5
Run
A
B
с
D
Resistivity
1
23
2
3
4
5
6
7
8
9
10
11
12
I+I+I+I+Oooo
0
0
||++TI++o000
33.2
4.6
31.2
9.6
40.6
162.4
39.4
158.6
63.4
62.6
58.7
0
0
60.9
3
(a) Estimate the factor effects. Plot the effect estimates on a normal probability scale.
(b) Identify a tentative model for this process. Fit the model and test for curvature.
(c) Plot the residuals from the model in part (b) versus the predicted resistivity. Is there any indication on
this plot of model inadequacy?
(d) Construct a normal probability plot of the residuals. Is there any reason to doubt the validity of the
normality assumption?
Stem1: 1,4
Stem 2: 2,4,8
Stem3: 2,4
Stem4: 0,1,6,8
Stem5: 0,1,2,3,9
Stem 6: 2,2
What’s the Min,Q1, Med,Q3,Max?
Chapter 6 Solutions
MindTap Business Statistics, 1 term (6 months) Printed Access Card for Anderson/Sweeney/Williams/Camm/Cochran's Essentials of Statistics for Business and Economics, 8th
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