Assumptions of linearity refer to the presumption that the relationship between independent and dependent variables is linear, which is crucial for the validity of linear regression models. Violations of this assumption can lead to inaccurate predictions and misleading statistical inferences, necessitating diagnostic checks and potential model adjustments.