What are the limitations of regression analysis?

What are the limitations of regression analysis?

Regression analysis is a powerful tool, but it has several limitations. For instance, it assumes a linear relationship between variables, which may not always be the case. Additionally, it can be sensitive to outliers, which can skew results.

Can you explain how multicollinearity affects regression analysis?

Certainly! Multicollinearity occurs when independent variables in a regression model are highly correlated. This can make it difficult to determine the individual effect of each variable on the dependent variable, leading to unreliable coefficient estimates.

What about the assumption of normality in regression?

The assumption of normality states that the residuals (errors) of the regression model should be normally distributed. If this assumption is violated, it can affect the validity of hypothesis tests and confidence intervals.

Are there any alternatives to regression analysis?

Yes, there are several alternatives, such as decision trees, random forests, and neural networks. These methods can handle non-linear relationships and interactions between variables more effectively.