<![CDATA[Multiple linear regression said to be good if it statistic theassumptions such as: normality assumption, heteroskedastisity, an errordoes not undergo autocorrelation and not occour multicolinearity. On theassumption that problem often arise in the multiple linear regressionassumptions are not fulfilled multicolinearity. Multicollinearity is acondition in which the data of the observations of the independent variablesoccuror have a relationship that is likely to be high. This study aimed tocompare the appropriate method to over come multicollinearity betweenridge regression and principal component analysis. Comparison criteriaused both methods, the mean square error (MSE) and the coefficient ofdetermination (R2), from the data is the simulation with Microsoft Excelthen the analysis was performed, in order to obtain the data first using ridgeregression has a value of MSE of 0.02405 and R2 of 82.4%, while theprincipal component analysis MSE value of 14.14 and R2of 37.5% while thedata second using ridge regression MSE has a value of 0.00216 and R2 of96.9%, while the principal component analysis MSE values of 5.15 and R2 of69.5%. From these results it can be concluded that ridge regression methodis better used.]]>
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