MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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The article "Experimental Design Approach for the Optimization of the Separation of
Enantiomers in Preparative Liquid Chromatography" (S. Lai and Z. Lin, Separation Science
and Technology, 2002: 847–875) describes an experiment involving a chemical process
designed to separate enantiomers. A model was fit to estimate the cycle time (y) in terms of
the flow rate (x1), sample concentration (x2), and mobile-phase composition (x3). The results
of a least-squares fit are presented in the following table. (The article did not provide the
value of the t statistic for the constant term.)
Predictor
Coefficient
т
Constant
1.603
X1
-0.619
-22.289
0.000
X2
0.086
3.084
0.018
0.306
11.011
0.000
0.272
8.542
0.000
0.057
1.802
0.115
0.105
3.300
0.013
X1X2
-0.022
-0.630
0.549
XXз
-0.036
-1.004
0.349
X>Xз
0.036
1.018
0.343
Of the following, which is the best next step in the analysis?
i.
Nothing needs to be done. This model is fine.
Drop x;, x;, and x from the model, and then perform an F test.
iii. Drop x1x2, X1X3,and x2x3 from the model, and then perform an F test.
iv. Drop x, and x; from the model, and then perform an F test.
Add cubic terms x, x, and x} to the model to try to improve the fit.
ii.
V.
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Transcribed Image Text:The article "Experimental Design Approach for the Optimization of the Separation of Enantiomers in Preparative Liquid Chromatography" (S. Lai and Z. Lin, Separation Science and Technology, 2002: 847–875) describes an experiment involving a chemical process designed to separate enantiomers. A model was fit to estimate the cycle time (y) in terms of the flow rate (x1), sample concentration (x2), and mobile-phase composition (x3). The results of a least-squares fit are presented in the following table. (The article did not provide the value of the t statistic for the constant term.) Predictor Coefficient т Constant 1.603 X1 -0.619 -22.289 0.000 X2 0.086 3.084 0.018 0.306 11.011 0.000 0.272 8.542 0.000 0.057 1.802 0.115 0.105 3.300 0.013 X1X2 -0.022 -0.630 0.549 XXз -0.036 -1.004 0.349 X>Xз 0.036 1.018 0.343 Of the following, which is the best next step in the analysis? i. Nothing needs to be done. This model is fine. Drop x;, x;, and x from the model, and then perform an F test. iii. Drop x1x2, X1X3,and x2x3 from the model, and then perform an F test. iv. Drop x, and x; from the model, and then perform an F test. Add cubic terms x, x, and x} to the model to try to improve the fit. ii. V.
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