Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:

 

Refer to the following nonlinear model which relates W to PQ, and R:

 

W = aPbQcRd

 

The computer output form the regression analysis is:

 

DEPENDENT VARIABLE: LNW R-SQUARE F-RATIO P-VALUE ON F
OBSERVATIONS: 18 0.9023 43.12 0.0001
VARIABLE   PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE
INTERCEPT   2.50 0.45 5.56 0.0001
LNP   -5.10 1.75 -2.91 0.0113
LNQ   12.40 3.20 3.88 0.0017
LNR   -6.00 1.50 -4.00 0.0010

 

Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:

 

 
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