In linear regression analysis, to obtain the best linear unbiased estimator, you need to perform a series of assumption tests. One of the assumption tests required in linear regression is the multicollinearity test.
If you analyze research data using linear regression, it is crucial to understand the required assumptions. Understanding these assumption tests is essential to ensure consistent and unbiased analysis results.
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.
Linear regression analysis has become one of the primary tools for researchers to explore the influence of independent variables on dependent variables. The Ordinary Least Squares (OLS) method has been a mainstay in conducting this linear regression analysis.
One of the assumptions in linear regression using the ordinary least square (OLS) method is that there is no strong correlation between independent variables. To get the Best Linear Unbiased Estimator in linear regression with ≥ 2 independent variables, you must be fulfilled the non-multicollinearity assumption.
