Question

13.17. Process yield. The yield (Y) of a chemical process depends on the temperature (X1) and pressure (X2). The following nonlinear regression model is expected to be applicable: Prior to beginning full-scale production, 18 tests were undertaken to study the process yield for various temperature and pressure combinations. The results follow. 18 100 100 398 16 17 2 100 100 43 100 128 32 103 a. To obtain starting values for yo, Vi, and V2, note that when we ignore the random error term, a logarithmic transformation yields Y- Po + AiX + B Xi2, where Y-log10 Yi, Po logo为, β,-M , Xİı = logo Xil, A = ½, and Xa = logo Xi2. Fit a first-order multiple regression model to the transformed data, and use as starting values go - antilogio bo, 8b, and 82 -b2 b. Using the starting values obtained in part (a), find the least squares estimates of the param eters ro, n, and ½

Full data set:
   12.0    1.0    1.0
   32.0   10.0    1.0
  103.0  100.0    1.0
   20.0    1.0   10.0
   61.0   10.0   10.0
  198.0  100.0   10.0
   38.0    1.0  100.0
  133.0   10.0  100.0
  406.0  100.0  100.0
    8.0    1.0    1.0
   38.0   10.0    1.0
   98.0  100.0    1.0
   14.0    1.0   10.0
   56.0   10.0   10.0
  205.0  100.0   10.0
   43.0    1.0  100.0
  128.0   10.0  100.0
  398.0  100.0  100.0
0 0
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Answer #1

X1 12 32 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2.00 2.00 2.00 0.00 0.00 0.00 1.00 1.00 1.00 2.00 2.00 2.00 SUMMARY OUTPUT The transformed equation is Yi Beta0+ Beta1 X1+Beta2 X2. The First order Multiple regression equation is Yi = 0.9819+0.5149X1+0.2985X2. 10 1.51 2.00 0.00 1.00 2.00 0.00 Regression Stotistics 1.30 1.79 2.30 Multiple F R Square Adjusted R Square Standard Error Observations 0.99 0.99 0.99 0.05 10 9.59 10nBeta0 0.51Betal 0.30 Beta2 2.12 2.61 0.90 2.00 0.00 ANOVA nificance F 661.97 2.34415-15 10 1.99 1-15 1.75 2.31 2.00 0.00 1.00 2.00 0.00 1.00 2.00 Regression 2.12 15 17 10 10 Total 4.30 Coefficients 0.9819 0.5149 0.2985 Standard Error 0.03 tStat P-value Lower 996 0.92 0.4B 0.26 Upper 95% Lower 95.0%Upper 95.0% 2.11 0.92 0.48 0.26 36.76 0.00 0.00 0.00 398 100 2.60 0.55 0.33 0.02 18.25 0.33Hope this clarifies your query.

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Full data set: 12.0 1.0 1.0 32.0 10.0 1.0 103.0 100.0 1.0 20.0 1.0 10.0 61.0...
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