Studentized residual distribution I read that in a regression with $k$ regressors, the t-statistic corresponding to a certain coefficient follows a $t(n-k)$ distribution. However, later on I read that studentized residuals follow a $t(n-k-1)$ distribution. How could it be that the extra degree of freedom is lost: isn't this also just based on a regular t-statistic?
 A: In my opinion there are two possible explanations here:


*

*Externally studentized residuals are based on data with one observation deleted, this may account for the loss of the single degree of freedom.

*There is inconsistency across books what $``k"$ actually refers to in a multiple regression model. If $k$ is the number of regressors, then the correct degrees of freedom is $n-k-1$. On the other hand, if $k$ is the number of regression coefficients (which is usually the number of regressors plus one, for the intercept), then the correct degrees of freedom is $n-k.$
Note: In general, the distribution of studentized residuals doesn't depend on whether there are dummy variables in the model or not. To be clear, let the regression model be $Y=X\beta +\epsilon$, where $X \in R^{n \times (k+1)}$, where $n$ is the number of observations and $k$ is the number of regressors. The design matrix $X$ may contain continuous variables, dummy variables and/or both. In this general framework, the internally Studentized residual is defined as
$$
r_i = \frac{e_i}{MSE(1-h_{ii})}
$$
where $e_i$ is the $i^{th}$ residual, $H=(h_{ij})=(X'X)^-X'Y$ is the so-called "hat" matrix. The internally Studentized residuals do not follow a $t$-distribution, because $e_i$ and $MSE$ are not independent.
The externally Studentized residual is defined as
$$
t_i = \frac{e_i}{MSE_{(i)}(1-h_{ii})}
$$
where $MSE_{(i)}$ is the mean-square error from the regression model fitted with the $i^{th}$ observation deleted. In this case, $e_i$ and $MSE_{(i)}$ are independent, and it can be shown that $t_i \sim t_{n-k-2}$, the loss of the extra one degree of freedom due to the deletion of observation $i$.
I hope this makes it clearer. So to understand the degrees of freedom in your case, you should consider the design matrix as a whole, and not break in into two parts - one with continuous predictors and one with dummy(s). Once you do that, figure out if the Studentized residuals in questions are externally or internally studentized. Then apply the above.
