In statistics,<unk>  is a regression method that models the relationship between a dependent variable Y, independent variables Xi, i = 1, ..., p, and a random term ε. The model can be written as
where 
β
0
{\displaystyle \beta _{0}}
 is the intercept ("constant" term), the 
β
i
{\displaystyle \beta _{i}}
s are the respective parameters of independent variables, and 
p
{\displaystyle p}
 is the number of parameters to be estimated in the<unk> .<unk>  can be contrasted with nonlinear regression.
This method is called "linear" because the relation of the response (the dependent variable 
Y
{\displaystyle Y}
) to the independent variables is assumed to be a linear function of the parameters. It is often erroneously thought that the reason the technique is called<unk> " is that the graph of 
Y
=
β
0
+
β
x
{\displaystyle Y=\beta _{0}+\beta x}
 is a straight line or that 
Y
{\displaystyle Y}
 is a linear function of the X variables. But if the model is (for example)
the problem is still one of<unk> , that is, linear in x and x2 respectively, even though the graph on 
x
{\displaystyle x}
 by itself is not a straight line.
Linear regression
Analysis of covariance
Box plot
Bayesian linear regression
Analysis of variance
Analysis of rhythmic variance
Likelihood-ratio test
Receiver operating characteristic