For linear regression to provide valid results, certain assumptions must be met: 1. Linearity: The relationship between the independent and dependent variables should be linear. 2. Independence: Observations should be independent of each other. 3. Homoscedasticity: Constant variance of the residuals. 4. Normality: The residuals should be normally distributed.
