The Application of the Least Squares Method to Multicollinear Data

Abstract

Regression analysis is an analysis that aims to determine whether there is a statistically dependent relationship between two variables, namely the predictor variable and the response variable. One of the methods for estimating multiple linear regression parameters is the Least Squares Method. Therefore, careful and meticulous analysis and selection of appropriate techniques are required to overcome the multicollinearity problem and ensure accurate and meaningful regression analysis results. Descriptive statistical table of response variables and predictor variables, where the average results are rounded. The regression equation using the OLS method is as follows: Y ̂=2,037+0.302X_1+0.206X_2+0.172X_3+0.342X_4. Therefore, it is important to use special techniques such as regularization or PCA to overcome the multicollinearity problem in the data before applying the least squares method. Thus, we can obtain more stable and accurate regression coefficient estimates and a more reliable linear regression model.