| dc.description.abstract | Regression analysis is one of statistic technics that used to determine the relation
model of one respon variable (Y) with one or more independent variable (X), what is
generally expressed in equation mathematic. In statistic, a regression model is
obtained by estimate of its parameter by using certain method, one of them is with the
Maximum Likelihood Methods. Regression model that obtained to be told is good or
fit, if fulfilled by the ideal assumption (classic). One of linear regression assumption
which must be fulfilled is homogeneity varian (variant from error have the character
of constant) so called also homoscedasticity. On the contrary, in reality if varian from
error is not constant for example big or minimize higher at value X, so the condition
told to heteroscedasticity or written down by: ar , , , . In
regression model if all classic assumption were fulfilled, except one of them was the
heteroscedasticity, so estimator that obtained still unbiased and consistent, but
inefficient (big varian). One of way to overcome the heteroscedasticity in regression
model is by Box Cox Transformation. Box Cox Transformation that is do the
transformation to respon variable Y which be ranked with the parameter , so that
become and estimator of parameter that obtained residing in gyration (-2,2). | en_US |
