Probability and Statistics for Engineering and the Sciences
9th Edition
ISBN: 9781305251809
Author: Jay L. Devore
Publisher: Cengage Learning
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Chapter 12.2, Problem 21E
Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbence, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinke resistance angle was read from a graph in the paper “Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy” (Textile Res. J., 1999: 145–151).
| x | .115 | .126 | .183 | .246 | .282 | .344 | .355 | .452 | .491 | .554 | .651 |
| y | 334 | 342 | 355 | 363 | 365 | 372 | 381 | 392 | 400 | 412 | 420 |
Here is regression output from Minitab:
| Predictor | Coef | SE Coef | T | P |
| Constant | 321.878 | 2.483 | 129.64 | 0.000 |
| absorb | 156.711 | 6.464 | 24.24 | 0.000 |
| source | DF | SS | MS | F | P |
| Regression | 1 | 7639.0 | 7639.0 | 587.81 | 0.000 |
| Residual Error | 9 | 117.0 | 13.0 | ||
| Total | 10 | 7756.0 |
- a. Does the simple linear regression model appear to be appropriate? Explain.
- b. What wrinkle resistance angle would you predict for a fabric specimen having an absorbance of .300?
- c. What would be the estimate of expected wrinkle resistance angle when absorbance is .300?
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Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle
resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with
Infrared Spectroscopy".†
x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651
y 334 342 355 363
365 372 381 392
400 412 420
Here is regression output from Minitab:
Predictor
Constant
absorb
S = 3.60498
Coef
321.878
156.711
SOURCE
Regression
Residual Error
Total
SE Coef
2.483
6.464
R-Sq = 98.5%
DF
1
9
10
SS
7639.0
117.0
7756.0
T
129.64
24.24
0.000
0.000
R-Sq (adj) = 98.3%
MS
7639.0
13.0
F
P
587.81
(a) Does the simple linear regression model appear to be…
Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical
strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t
半
0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651
334 342
355
363
365
372
381
392
400
412
420
Here is regression output from Minitab:
Predictor
Coef
SE Coef
P
Constant
321.878
2.483
129.64
0.000
absorb
156.711
6.464
24.24
0.000
S = 3.60498
R-Sq = 98.5%
R-Są (adj) - 98.3%
SOURCE
DF
MS
F
P
Regression
1
7639.0
7639.0
587.81
0.000
Residual Error
9
117.0
13.0
Total
10
7756.0
(a) Does the simple linear regression model appear to be…
Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as
determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and
y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t
x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651
y
334 342 355
363
365 372 381
400
392
412 420
Here is regression output from Minitab:
Predictor
Constant
absorb
S = 3.60498
Coef
321.878
156.711
SOURCE
Regression
Residual Error
Total
R-Sq= 98.5%
DF
SE Coef
2.483
6.464
1
9
10
SS
7639.0
117.0
7756..0
T
129.64
24.24
P
0.000
0.000.
R-Sq (adj) 98.3%
MS
7639.0
13.0
F
587.81
(a) Does the simple linear regression model appear to be appropriate?…
Chapter 12 Solutions
Probability and Statistics for Engineering and the Sciences
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