Generalized-Linear-Models.md

@@ -62,8 +62,17 @@ When constructing a generalized linear model, three major decisions must be made
                  target="_blank"><img src="https://latex.codecogs.com/svg.latex? 
                  P(y;&space;\mu)&space;=&space;\frac{e^{-\mu}\mu^y}{y!}" title="P(y; \mu) = 
                  \frac{e^{-\mu}\mu^y}{y!}" /></a>
-      * Bernoulli 
-      * Binomial 
+
+      * Bernoulli: <a href="https://www.codecogs.com/eqnedit.php? 
+                   latex=P(y;&space;\pi)=\pi^y(1-\pi)^{1-y}" target="_blank"><img 
+                   src="https://latex.codecogs.com/svg.latex?P(y;&space;\pi)=\pi^y(1- 
+                   \pi)^{1-y}" title="P(y; \pi)=\pi^y(1-\pi)^{1-y}" /></a>
+
+      * Binomial: <a href="https://www.codecogs.com/eqnedit.php? 
+                  latex=P(y;&space;\pi,n)=\binom{n}{y}\pi^y&space;(1-\pi)^{n-y}" 
+                  target="_blank"><img src="https://latex.codecogs.com/svg.latex? 
+                  P(y;&space;\pi,n)=\binom{n}{y}\pi^y&space;(1-\pi)^{n-y}" title="P(y; 
+                  \pi,n)=\binom{n}{y}\pi^y (1-\pi)^{n-y}" /></a>
 
     We should note that this distribution is not the *true* distribution of the population, 
     but is a, approximation of the distribution of response variable. Each natural