Linearity: The relationship between the independent and dependent variables should be linear, meaning that changes in the independent variable result in proportional changes in the dependent variable.

Independence: The observations should be independent of each other, meaning that the value of one observation does not influence another.

Homoscedasticity: The variance of the residuals (errors) should be constant across all levels of the independent variable, meaning that the spread of errors should be roughly the same for all predicted values.

Normality: The residuals should be approximately normally distributed, especially for small sample sizes. This means that the errors should form a bell-shaped curve when plotted.

No multicollinearity: In the case of multiple regression, the independent variables should not be highly correlated with each other, as this can make it difficult to determine the individual effect of each variable.