How is the standard error of estimate (SEE) typically computed in a simple linear regression model?

The standard error of estimate (SEE), often denoted as `s_e` or `s_yx`, is computed as the square root of the Mean Squared Error (MSE) of the regression. For a simple linear regression model, the formula is `sqrt(SSE / (n - 2))`, where `SSE` is the sum of squared errors (residuals), `n` is the number of observations, and `(n - 2)` represents the degrees of freedom. In multiple linear regression, the denominator becomes `(n - k - 1)`, where `k` is the number of independent variables.