User Guide

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Chapter 26

Standardized. A transformation of each predicted value into its standardized
form. That i
s, the mean predicted value is subtracted from the predicted value,
and the difference is divided by the standard deviation of the predicted values.
Standardized predicted values have a mean of 0 and a standard deviation of 1.
 Adjusted. Thepredictedvalueforacasewhenthatcaseisexcludedfromthe
calculatio
n of the regression coefficients.
 S.E. of mea
n predictions.
Standard errors of the predicted values. An estimate of
the standard deviation of the average value of the dependent variable for cases
that have the same values of the independent variables.
Distances. Measures to identify cases with unusual combinations of values for the
independe
nt variables and cases that may have a large impact on the regression model.
 Mahalano
bis.
A measure of how much a case's values on the independent variables
differ from the average of all cases. A large Mahalanobis distance identifies a
case as having extreme values on one or more of the independent variables.
 Cook's. A measure of how much the residuals of all cases would change if a
particula
r case were excluded from the calculation of the regression coefficients.
A large Cook's D indicates that excluding a case from computation of the
regression statistics changes the coefficients substantially.
 Leverage values. Measures the influence of a point on the fit of the regression.
The cente
red leverage ranges from 0 (no influence on the fit) to (N-1)/N.
Prediction Intervals. The upper and lower bounds for both mean and individual
prediction intervals.
 Mean. Lower and upper bounds (two variables) for the prediction interval of
the mean predicted response.
 Individual. Lower and upper bounds (two variables) for the prediction interval of
the depe
ndent variable for a single case.
 Confide
nce Interval.
Enter a value between 1 and 99.99 to specify the confidence
level for the two Prediction Intervals. Mean or Individual must be selected before
entering this value. Typical confidence interval values are 90, 95, and 99.
Residuals. The actual value of the dependent variable minus the value predicted by
the regr
ession equation.
 Unstan
dardized.
The difference between an observed value and the value predicted
by the model.