You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Almost anything will work, but why not take the simple example x = y = 1? If square roots behaved as this formula asserts then we would have √2 = √1 + √1 = 1 + 1 = 2, which is clearly absurd.