4.
Process Modeling
4.6. Case Studies in Process Modeling 4.6.2. Alaska Pipeline


Weighting  Another approach when the assumption of constant standard deviation of the errors (i.e. homogeneous variances) is violated is to perform a weighted fit. In a weighted fit, we give less weight to the less precise measurements and more weight to more precise measurements when estimating the unknown parameters in the model.  
Fit for Estimating Weights 
For the pipeline data, we chose
approximate replicate groups
so that each group has four observations (the last
group only has three). This was done by first
sorting the data by the predictor variable and then
taking four points in succession to form each
replicate group.
Using the power function model with the data for estimating the weights, the following results for the fit of ln(variances) against ln(means) for the replicate groups were generated. Parameter Estimate Stan. Dev t Value B0 3.18451 0.8265 3.9 B1 1.69001 0.2344 7.2 Residual standard deviation = 0.85612 Residual degrees of freedom = 25 The numerical fitting results and the plot of the replicate variances against the replicate means shows that a linear fit provides a reasonable fit with an estimated slope of 1.69. We used an estimate of 1.5 for the exponent in the weighting function. 

Residual Plot for Weight Function 
The residual plot from the fit to determine an appropriate weighting function reveals no obvious problems. 

Numerical Results from Weighted Fit 
The weighted fit of the model that relates the field measurements
to the lab measurements is shown below.
Parameter Estimate Stan. Dev t Value B0 2.35234 0.54312 4.3 B1 0.80636 0.02265 35.6 Residual standard deviation = 0.36459 Residual degrees of freedom = 105The resulting slope and intercept are 0.81 and 2.35, respectively. These are compared to a slope of 0.73 and an intercept of 4.99 in the original model. 

Plot of Predicted Values 
The plot of the predicted values with the data indicates a good fit. 

Diagnostic Plots of Weighted Residuals 
We need to verify that the weighting did not result in the other regression assumptions being violated. A 6plot, after weighting the residuals, indicates that the regression assumptions are satisfied. 

Plot of Weighted Residuals vs Lab Defect Size 
In order to check the assumption of homogeneous variances for the errors in more detail, we generate a full sized plot of the weighted residuals versus the predictor variable. This plot suggests that the errors now have homogeneous variances. 