Including higher-order terms of a numeric explanatory variable in a model

Higher-order terms refer to polynomials constructed from a numeric variable
by raising that variable to one or more powers. For example, the higher-order
terms of a variable called age would be (age)^{2},
(age)^{3}, etc. Because polynomial powers can form a large
number of smooth shapes, including higher-order terms in a model can provide a
better model fit and account for some non-linearity in the relationship between
an explanatory variable and the outcome variables. However, including
higher-order terms without a prior justification may add unnecessary complexity
to the model and ultimately reduce its predictive power by increasing the
number of model coefficients relative to the number of observations.

To include higher-order terms in a model:

In the Model view, select the numeric variable for which
you wish to add higher-order terms

Choose Model > Explanatory Variable > Higher Order Terms, and
select the terms you wish to include

Up to four higher-order terms (up to a fifth power) may be included.
Predictions will automatically take the included higher-order terms into
account.