# Conditional Logic (Nonlinear Regression)

You can specify a segmented model using conditional logic. To use conditional logic within a model expression or a loss function, you form the sum of a series of terms, one for each condition. Each term consists of a logical expression (in parentheses) multiplied by the expression that should result when that logical expression is true.

For example, consider a segmented model that equals 0 for X<=0, X for 0<X<1, and 1 for X>=1. The expression for this is:

(X<=0)*0 + (X>0 & X<1)*X + (X>=1)*1.

The logical expressions in parentheses all evaluate to 1 (true) or 0 (false). Therefore:

If X<=0, the above reduces to 1*0 + 0*X + 0*1 = 0.

If 0<X<1, it reduces to 0*0 + 1*X + 0*1 = X.

If X>=1, it reduces to 0*0 + 0*X + 1*1 = 1.

More complicated examples can be easily built by substituting different logical expressions and outcome expressions. Remember that double inequalities, such as 0<X<1, must be written as compound expressions, such as (X>0 & X<1).

String variables can be used within logical expressions:

(city='New York')*costliv + (city='Des Moines')*0.59*costliv

This yields one expression (the value of the
variable *costliv*) for New Yorkers
and another (59% of that value) for Des Moines residents. String constants
must be enclosed in quotation marks or apostrophes, as shown here.