4.
Process Modeling
4.6. Case Studies in Process Modeling 4.6.1. Load Cell Calibration


Least Squares Estimation  Using software for computing least squares parameter estimates, the straightline model, $$ D = \beta_0 + \beta_1L + \varepsilon \, ,$$ is easily fit to the data. The regression results are shown below. Before trying to interpret all of the numerical output, however, it is critical to check that the assumptions underlying the parameter estimation are met reasonably well. The next two sections show how the underlying assumptions about the data and model are checked using graphical and numerical methods.  
Regression Results 
Parameter Estimate Stan. Dev t Value B0 0.614969E02 0.7132E03 8.6 B1 0.722103E06 0.3969E09 0.18E+04 Residual standard deviation = 0.0021712694 Residual degrees of freedom = 38 Lackoffit F statistic = 214.7464 Lackoffit critical value, F_{0.05,18,20} = 2.15 