In multiple linear regression analysis, there is an assumption that the model constructed is not affected by multicollinearity issues, where two or more independent variables are strongly correlated. Multicollinearity can lead to errors in parameter estimation and reduce the reliability of the model.
Multicollinearity detection is one of the assumption tests that must be performed on multiple linear regression. This assumption test was conducted to obtain the best linear unbiased estimator.
When analyzing data using linear regression using the Ordinary Least Square (OLS) method, it takes an understanding of the assumption test that must be passed. The non-multicollinearity test is necessary to get the best linear unbiased estimator. The multiple linear regression OLS method has been widely applied in various fields: economics, agribusiness, and socio-economic fields. …
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