User`s guide

8 Model Analysis
Using Residual Analysis Plots to Validate Models
In this section...
“What Is Residual Analysis?” on page 8-16
“Supported Model Types” on p age 8-17
“What Does the Residuals P lot Show?” on page 8-17
“Displaying the Confidence Interval” on page 8-18
“How to Plot Residuals UsingtheGUI”onpage8-19
“How to Plot Residuals at the Command Line” on page 8-21
“Example – Examining Model Residuals” on page 8-21
What Is Residual Analysis?
Residuals are differences between the one-step-predicted output from the
model and the measured output from the validation data set. Thus, residuals
represent the portion of the validation data not explained by the model.
Residual analysis consists of two tests: the whiteness test and the
independence test.
According to the whiteness test criteria, a good m odel has the residual
autocorrelation function inside the confidence interval of the corresponding
estimates, indicating that theresidualsareuncorrelated.
According to the independence test criteria, a good model has residuals
uncorrelated with past inputs. Evidence of correlation indicates that the
model does not describe how part of the output relates to the corresponding
input. For example, a peak outside the confidence interval for lag k means
that the output y(t) that originates from the input u(t-k) is not properly
described by the m odel.
Your model should pass both the whiteness and the independence tests,
except in the following cases:
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