A survey was conducted of 130 purchases of new BMW 3 series cars, 130 purchasers of new BNIVV 5 series cars, and 130 purchasers of new BMW 7 series cars. In it, people were asked the age they were when they purchased their car. The following box plots display the results. Figure 2.46 In complete sentences, describe what the of each box plot implies about the distribution of the data collected for that car series. b. Which group is most likely to have an outlier? Explain how you determined that. c. Compare the three box plots. What do they simply about the age of purchasing a BMW from the series when compared to each other? d. Look at the BMW 5 series. Which quarter has the smallest spread of data? What is the spread? e. Look at the BMW 5 series. Which quarter has the largest spread of data? What is the spread? f. Look at the BMW 5 series. Estimate the interquartile range (IQR). g. Look at the BMW 5 series. Are there more data in the interval 31 to 38 or in the interval 45 to 55? How do you know this? h. Look at the BMW 5 series. Which interval has the fewest data in it? How do you know this? i. 31-35 ii. 38-41 iii. 41-64
A survey was conducted of 130 purchases of new BMW 3 series cars, 130 purchasers of new BNIVV 5 series cars, and 130 purchasers of new BMW 7 series cars. In it, people were asked the age they were when they purchased their car. The following box plots display the results. Figure 2.46 In complete sentences, describe what the of each box plot implies about the distribution of the data collected for that car series. b. Which group is most likely to have an outlier? Explain how you determined that. c. Compare the three box plots. What do they simply about the age of purchasing a BMW from the series when compared to each other? d. Look at the BMW 5 series. Which quarter has the smallest spread of data? What is the spread? e. Look at the BMW 5 series. Which quarter has the largest spread of data? What is the spread? f. Look at the BMW 5 series. Estimate the interquartile range (IQR). g. Look at the BMW 5 series. Are there more data in the interval 31 to 38 or in the interval 45 to 55? How do you know this? h. Look at the BMW 5 series. Which interval has the fewest data in it? How do you know this? i. 31-35 ii. 38-41 iii. 41-64
A survey was conducted of 130 purchases of new BMW 3 series cars, 130 purchasers of new BNIVV 5 series cars, and 130 purchasers of new BMW 7 series cars. In it, people were asked the age they were when they purchased their car. The
following box plots display the results.
Figure 2.46
In complete sentences, describe what the of each box plot implies about the distribution of the data collected for that car series.
b. Which group is most likely to have an outlier? Explain how you determined that.
c. Compare the three box plots. What do they simply about the age of purchasing a BMW from the series when compared to each other?
d. Look at the BMW 5 series. Which quarter has the smallest spread of data? What is the spread?
e. Look at the BMW 5 series. Which quarter has the largest spread of data? What is the spread?
f. Look at the BMW 5 series. Estimate the interquartile range (IQR).
g. Look at the BMW 5 series. Are there more data in the interval 31 to 38 or in the interval 45 to 55? How do you know this?
h. Look at the BMW 5 series. Which interval has the fewest data in it? How do you know this?
-
+
++
Table 2: Crack Experiment for Exercise 2
A B C D Treatment Combination
(1)
Replicate
I II
7.037
6.376
14.707 15.219
|++++ 1
བྱ॰༤༠སྦྱོ སྦྱོཋཏྟཱུ
a
b
ab
11.635 12.089
17.273 17.815
с
ас
10.403 10.151
4.368 4.098
bc
abc
9.360 9.253
13.440 12.923
d
8.561 8.951
ad
16.867 17.052
bd
13.876 13.658
abd
19.824 19.639
cd
11.846 12.337
acd
6.125
5.904
bcd
11.190 10.935
abcd
15.653 15.053
Question 3
Continuation of Exercise 2. One of the variables in the experiment described in Exercise 2, heat treatment
method (C), is a categorical variable. Assume that the remaining factors are continuous.
(a) Write two regression models for predicting crack length, one for each level of the heat treatment method
variable. What differences, if any, do you notice in these two equations?
(b) Generate appropriate response surface contour plots for the two regression models in part (a).
(c) What set of conditions would you recommend for the factors A, B, and D if you use heat treatment method
C = +?
(d) Repeat…
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?
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