Generalized-Linear-Models.md

@@ -72,18 +72,28 @@ When constructing a generalized linear model, three major decisions must be made
                  latex=P(y;&space;\mu)&space;=&space;\frac{e^{-\mu}\mu^y}{y!}" 
                  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>
+                 \frac{e^{-\mu}\mu^y}{y!}" /></a> &nbsp;&nbsp; for <a 
+                 href="https://www.codecogs.com/eqnedit.php?latex=y=0,1,..." 
+                 target="_blank"><img src="https://latex.codecogs.com/svg.latex?y=0,1,..." 
+                 title="y=0,1,..." /></a>
 
       * 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>
+                   \pi)^{1-y}" title="P(y; \pi)=\pi^y(1-\pi)^{1-y}" /></a> &nbsp;&nbsp; for 
+                   <a href="https://www.codecogs.com/eqnedit.php?latex=y=0,1" t 
+                   arget="_blank"><img src="https://latex.codecogs.com/svg.latex?y=0,1" 
+                   title="y=0,1" /></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>
+                  \pi,n)=\binom{n}{y}\pi^y (1-\pi)^{n-y}" /></a> &nbsp;&nbsp; for <a 
+                  href="https://www.codecogs.com/eqnedit.php? 
+                  latex=y=0,1,&space;...,&space;n" target="_blank"><img 
+                  src="https://latex.codecogs.com/svg.latex?y=0,1,&space;...,&space;n" 
+                  title="y=0,1, ..., n" /></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