The decomposition theorem in linear regression models helps break down how different factors affect the results. Multicollinearity, when variables are too closely related, can make it hard to interpret the data. Sometimes, important factors may not seem significant due to how the data is structured. Current methods for detecting multicollinearity may not be reliable. Increasing the sample size can help understand the causes better.