How do I know if linear regression is appropriate for my data?

Linear regression is appropriate when four key assumptions are met: (1) Linearity — the relationship between the dependent and independent variables should be linear. (2) Normality — the residuals (the difference between the observed and predicted values) must be normally distributed.

(3) Homoscedasticity — variances of residuals should remain constant across all levels of the independent variable. (4) No Multicollinearity — independent variables must not correlate closely with each other. Check these assumptions using scatter plots, residual plots, and tests like the Shapiro-Wilk test before proceeding.