In time series data analyzed using multiple linear regression with the ordinary least squares (OLS) method, it is also necessary to test for multicollinearity. The multicollinearity test is one of the assumption tests to ensure the best linear unbiased estimator.
The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.
The normality test in multiple linear regression analysis is aimed at detecting whether the residuals are normally distributed. In research using time series data, it is also necessary to perform a normality test to ensure that the required assumptions are met.
In linear regression analysis using the Ordinary Least Square method, it is necessary to ensure that there is no strong correlation between independent variables. To obtain the best linear unbiased estimator, there must not be a strong correlation between the independent variables.
Heteroskedasticity testing is an assumption test in linear regression using the OLS method to ensure that the residual variance is constant. A constant residual variance is referred to as homoskedasticity.
