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  1. Hi all, I know that, at least in linear regression (simple linear and multiple) we assume : Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. Normality: For any fixed value of X, Y is normally distributed Normality of residuals should tell us if the regression model is strong. I wonder if this is valid still for non-linear regression and, in general, which are properties of residuals for non linear regression compared with linear regression. Thanks
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