Can you explain the assumptions underlying linear regression?
- Independence of features, Normality of target variable, Linearity of relationship, Constant variance
- Normal distribution of errors, Linearity of relationship, Independence of residuals, Constant variance
- Normality of residuals, Constant variance, Independence of residuals, Linearity of relationship
- Normality of residuals, Linearity of relationship, Multicollinearity, Independence of features
Linear regression assumes that the relationship between the dependent and independent variables is linear, errors are normally distributed, residuals are independent, and the variance of residuals is constant across all levels of the independent variables. These assumptions guide the model's performance and interpretation.
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