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

If linear regression is applicable in our case, it is necessary to first ascertain if certain key assumptions hold.

• Linearity: The resemblance between the dependent and independent variables should be one of linearity.
• Normality: The residuals (the difference between the observed and the predicted values) must be normally distributed. Normality can be assessed through histograms or other normality tests.
• Homoscedasticity: Variances of residuals should remain constant across all levels of the independent variable, and the residual plot patterns should be checked.
• No Multicollinearity: Independent variables must not correlate closely with each other.

Once the preceding assumptions are satisfied, we are now in a position to conduct our linear regression analysis.