Generalized-Linear-Models.md

@@ -160,7 +160,17 @@ In this linear predictor, we can also use transformation of the predictors as we
     as logistic (logit function), normal (probit function), or Gumbal (log-log function) 
     distributions). 
 
-    For the distributions of the natural exponential family, special link functions exist which are called *Canonical Link Functions*. By using the canonical link functions, we assure that the natural parameter of the distribution <img src="https://latex.codecogs.com/svg.latex?\theta" title="\theta" /> equals the linear predictor <img src="https://latex.codecogs.com/svg.latex?\eta" title="\eta" />. These functions are a good initial choice to start our modeling procedure but in some cases they may not be the best option. 
+    For the distributions of the natural exponential family, special link functions exist 
+    which are called *Canonical Link Functions*. By using the canonical link functions, we 
+    assure that the natural parameter of the distribution <img 
+    src="https://latex.codecogs.com/svg.latex?\theta" title="\theta" /> equals the linear 
+    predictor <img src="https://latex.codecogs.com/svg.latex?\eta" title="\eta" />. These 
+    functions are a good initial choice to start our modeling procedure but in some cases 
+    they may not be the best option. 
+
+In summary, a generalized linear model can be expressed as: 
+
+<img src="https://latex.codecogs.com/svg.latex?y_i&space;\sim&space;f(y|\mu_i,\phi)\\&space;g(\mu_i)=\eta_i\\&space;\eta_i=\sum_q{\beta_qx_{iq}}=\mathbf{\beta^{'}x}_i" title="y_i \sim f(y|\mu_i,\phi)\\ g(\mu_i)=\eta_i\\ \eta_i=\sum_q{\beta_qx_{iq}}=\mathbf{\beta^{'}x}_i" />