text: Why are residuals Normally distributed?
  
  When they assume the $$\varepsilon_i$$ have a normal distribution, with mean $$0$$ and variance $$\sigma^2,$$ then the estimated errors $$\hat\varepsilon$$ have a normal distribution with mean $$0$$ and variance $$\operatorname{Var}(\hat\varepsilon).$$
  
  But what is the connection? Why, when errors are normally distributed, the estimated residuals also are normally distributed?
  
  • Because this is a statistics site and we have been around a fairly long time, you can find a great deal written about this with a site search. The key phrase "hat matrix" is especially helpful.
  – whuber
  Commented Jul 13, 2022 at 13:36
  
  The connection is made through a specific procedure: namely, ordinary least squares. When you suppose a response $$Y$$ is a random variable related to other variables $$X$$ in the form
  
  $$Y = X\beta + \varepsilon$$
  
  where $$\varepsilon$$ is assumed to have a joint Normal distribution (plus some other assumptions that don't matter here), then the least squares solution is
  
  $$\hat\beta = (X^\prime X)^{-}X^\prime Y = (X^\prime X)^{-}X^\prime (X\beta + \epsilon) = \beta + J\varepsilon$$
  
  for a matrix $$J = (X^\prime X)^{-}X^\prime$$ that is computed only from $$X.$$ The residuals, or estimated errors, are the differences
  
  $$\hat\varepsilon = Y - \hat Y = (X\beta + \varepsilon) - X\hat\beta = \varepsilon - XJ\varepsilon = (\mathbb I - H)\varepsilon$$
  
  where $$H = XJ = X(X^\prime X)^{-}X^\prime$$ is the "hat matrix." This exhibits the residuals as linear combinations of $$\varepsilon$$ (with coefficients given by $$\mathbb I - H$$). Linear combinations of jointly Normal variables are Normal, QED.
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