In statistics, the<unk>  is used to test for heteroskedasticity in a linear regression model.  It tests whether the estimated variance of the residuals from a regression are dependent on the values of the independent variables.
Suppose that we estimate the equation
We can then estimate 
u
^
{\displaystyle {\hat {u}}}
, the residual.  Ordinary least squares constrains these so that their mean is 0, so we can calculate the variance as the average squared values.  Even simpler is to simply regress the squared residuals on the independent variables, which is the<unk> :
If an F-test confirms that the independent variables are jointly significant then we can reject the hypothesis of no heteroskedasticity.
The<unk>  tests for conditional heteroskadicity.  It is a chi-squared test: the test statistic is  nχ2 with k degrees of freedom.  If the<unk>  shows that there is conditional heteroskadicity, it can be corrected by using the Hansen method, using robust standard errors, or re-thinking the regression equation.
Breusch-Pagan test
Schober's test
Tine test
Anderson-Darling test
Denver Scale
Heaf test
Unterberger test
Patrick's test