The tolerance value and Variance Inflation Factor (VIF) are important metrics that you can use to detect multicollinearity among independent variables. If we recall the basic theory, multicollinearity testing is an assumption test in the Ordinary Least Squares (OLS) regression method, which aims to ensure that there is no strong correlation between independent variables.
In multiple linear regression analysis using cross-sectional data, there are several assumption tests that must be conducted to obtain the best linear unbiased estimator. It is crucial to understand which assumption tests are required for research utilizing cross-sectional data. This is important because the assumption tests for cross-sectional, time series, and panel data differ in …
Assumption Tests for Multiple Linear Regression on Cross-Sectional Data Read More »
Multiple linear regression with the Ordinary Least Squares (OLS) method is one of the statistical techniques used to assess the influence of two or more independent variables on a dependent variable. The OLS method is carried out by minimizing the sum of squared errors between the model’s predictions and the actual values of the dependent …
Multiple linear regression equations must fulfill the required assumptions to obtain the best linear unbiased estimator. Several assumptions in linear regression using the ordinary least square (OLS) method must be met, one of which is non-multicollinearity.
When choosing multiple linear regression analysis, we include at least two independent variables into the model. To obtain the best linear unbiased estimator, we must test the assumptions. One of the assumptions that need to be tested is the multicollinearity test.
