# Are autocorrelation, heteroscedasticity and normality of residuals used in non-linear models?

I have a very simple non-linear model:

lm(Y ~ poly(X, degree = 2), data=data)


The results I obtained are:

My question is, to test the validity of the model, is testing for autocorrelation, heteroscedasticity and normal distribution of residuals relevant for non-linear models?

You do not have a nonlinear model, you have a linear model with a polynomial predictor. So yes, this model is nonlinear in the predictor x, but it is linear in the unknown model parameters, and that is what is meant with a linear model.