corrected some pages

Several small corrections were done to several proofs and definitions.

Author: JoramSoch

P/bin-test.md

@@ -88,7 +88,7 @@ $$ \label{eq:y-cvals}
 \begin{split}
 \alpha &\geq \mathrm{Pr}(y \in \left\lbrace 0, \ldots, y_1 \right\rbrace \cup \left\lbrace y_2, \ldots, n \right\rbrace \vert H_0) \\
 &= \mathrm{Pr}(y \leq y_1 \vert H_0) + \mathrm{Pr}(y \geq y_2 \vert H_0) \\
-&= \mathrm{Pr}(y \leq y_1 \vert H_0) + (1-\mathrm{Pr}(y \leq (y_2-1) \vert H_0) \; .
+&= \mathrm{Pr}(y \leq y_1 \vert H_0) + \left( 1 - \mathrm{Pr}(y \leq (y_2-1) \vert H_0) \right) \; .
 \end{split}
 $$
 

P/prob-exc.md

@@ -40,7 +40,7 @@ $$ \label{eq:exc}
 p(A,B) = 0 \; .
 $$
 
-The [addition law of probability](/D/prob-marg) states that
+The [addition law of probability](/D/prob-add) states that
 
 $$ \label{eq:prob-add-set}
 p(A \cup B) = p(A) + p(B) - p(A \cap B)

P/ugkv-ztest2.md

@@ -79,7 +79,7 @@ $$ \label{eq:mean-samp-dist}
 \end{split}
 $$
 
-Again employing the linear combination theorem, the distribution of $z = [(\bar{y}_1-\bar{y}_2)-\mu_\Delta]/\sigma_\Delta$ becomes
+Again employing the linear combination theorem, the distribution of $z = \left[ (\bar{y}\_1-\bar{y}\_2) - \mu_{\Delta} \right] / \sigma_{\Delta}$ becomes
 
 $$ \label{eq:z-dist-s1}
 z = \frac{(\bar{y}_1-\bar{y}_2)-\mu_\Delta}{\sigma_\Delta} \sim \mathcal{N}\left( \frac{(\mu_1-\mu_2)-\mu_\Delta}{\sigma_\Delta}, \left(\frac{1}{\sigma_\Delta}\right)^2 \sigma_\Delta^2 \right) = \mathcal{N}\left( \frac{(\mu_1-\mu_2)-\mu_\Delta}{\sigma_\Delta}, 1 \right)