A regression model specifies how the conditional distribution of a response variable 
depends on one or more explanatory
variables 
.
A common representation is
 |
where 
is the regression function and the error 
has conditional expectation
value zero. Parametric regression assumes
a finite-dimensional form for 
, such as a linear regression,
while nonparametric regression estimates 
with fewer structural assumptions.
