How is a linear model appropriate?

A linear model is appropriate when there is a linear relationship between the independent and dependent variables, meaning that changes in the independent variable consistently result in proportional changes in the dependent variable. It is also suitable when the residuals (the differences between observed and predicted values) are normally distributed and exhibit homoscedasticity, or constant variance. Additionally, linear models are easy to interpret and computationally efficient, making them a good choice for many real-world applications where relationships can be approximated as linear.