In this study, the widely used Coldfeld-C^uandt test for lieterosk('da.sticity in the linear regression model is evaluated. VV(' reduce the dimension of the data spa.ce that is needed lor tin' computaticui of tlu' t('sts. VVe tlu'ii compa.r(‘ the pi'rformaiK'es of tin' Likelihood Ratio and tin* Cloldh'ld-C^uandt tests by using stringency measure. The problem of analytically non-tractable distribution function in the case of the Likelihood Ratio test is overcome by employing Monte Carlo methods. It is observed that the Likelihood Ratio test is better in most of the cases than the Goldfeld-Quandt test.