The article explores how collinearity affects the accuracy of multiple regression analysis. By examining the relationships between different variables, the researchers found that collinearity can make variables less independent and more redundant, leading to inaccurate results. They used various methods like correlation analysis to detect collinearity and found it to be severe in their model. This collinearity inflated the variance of estimates and caused errors in interpreting the data. Although complete elimination of collinearity is not possible, reducing its intensity can improve the performance of the variables and error terms in the model.