4. Process Modeling 4.6. Case Studies in Process Modeling 4.6.1. Load Cell Calibration 4.6.1.8. Graphical Residual Analysis - Model #2
	The data with a quadratic estimated regression function and the residual plots are shown below.
Compare to Initial Model
	This plot is almost identical to the analogous plot for the straight line model, again illustrating the lack of detail in the plot due to the scale. In this case, however, the residual plots will show that the model does fit the well.
Plot Indicates Model Fits Well
	The randomly scattered residuals, centered around zero, in this plot indicate that the quadratic is a good function to describe this data. There is also no indication of non-constant variability over the range of loads.
Plot Also Indicates Model OK
	This plot also looks good. There is no evidence of changes in variability across the range of deflection.
No Problems Indicated
	All of these residual plots have been corrected by simply changing the functional form of the model. There is no evidence in the run order plot of any time dependence in the measurement process, and the lag plot indicates that the random errors are independent from one measurement to the next. The histogram and normal probability plot suggest that the random errors affecting the measurement process are normally distributed.