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Hi all,

I know that, at least in linear regression (simple linear and multiple) we assume :

  1. Linearity: The relationship between X and the mean of Y is linear.
  2. Homoscedasticity: The variance of residual is the same for any value of X.
  3. Independence: Observations are independent of each other.
  4. 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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