When should I use Ordinary Least Squares (OLS) regression?

Can you explain when it's appropriate to use Ordinary Least Squares regression?

Ordinary Least Squares regression is appropriate when you have a linear relationship between your independent and dependent variables, and your data meets certain assumptions like homoscedasticity and normality.

What are the assumptions I need to check before using OLS?

You should check for linearity, independence, homoscedasticity, normality of residuals, and no multicollinearity among predictors.

Are there situations where OLS might not be the best choice?

Yes, OLS may not be suitable if your data has outliers, is not normally distributed, or if the relationship is not linear. In such cases, consider using robust regression or other methods.

How can I check for multicollinearity?

You can check for multicollinearity using Variance Inflation Factor (VIF) or by examining correlation matrices among your independent variables.