Merge pull request sux13#4 from LukeBrumfield/master

Regression Models Notation

Author: sux13

7_REGMODS/Regression Models Course Notes HTML.Rmd

@@ -133,7 +133,7 @@ g
 
 
 ### Derivation for Least Squares = Empirical Mean (Finding the Minimum)
-* Let $X_i = **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
+* Let $X_i =$ **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
 * Proof is as follows $$
 \begin{aligned}
 \sum_{i=1}^n (Y_i - \mu)^2 & = \sum_{i=1}^n (Y_i - \bar Y + \bar Y - \mu)^2 \Leftarrow \mbox{added}  \pm \bar Y \mbox{which is adding 0 to the original equation}\\

7_REGMODS/Regression Models Course Notes.Rmd

@@ -131,7 +131,7 @@ g
 $\pagebreak$
 
 ### Derivation for Least Squares = Empirical Mean (Finding the Minimum)
-* Let $X_i = **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
+* Let $X_i =$ **regressor**/**predictor**, and $Y_i =$ **outcome**/**result** so we want to minimize the the squares: $$\sum_{i=1}^n (Y_i - \mu)^2$$
 * Proof is as follows $$
 \begin{aligned}
 \sum_{i=1}^n (Y_i - \mu)^2 & = \sum_{i=1}^n (Y_i - \bar Y + \bar Y - \mu)^2 \Leftarrow \mbox{added}  \pm \bar Y \mbox{which is adding 0 to the original equation}\\

7_REGMODS/Regression_Models_Course_Notes_HTML.html

@@ -348,7 +348,7 @@ <h2>Dalton’s Data and Least Squares</h2>
 <div id="derivation-for-least-squares-empirical-mean-finding-the-minimum" class="section level3">
 <h3>Derivation for Least Squares = Empirical Mean (Finding the Minimum)</h3>
 <ul>
-<li>Let $X_i = <strong>regressor</strong>/<strong>predictor</strong>, and <span class="math">\(Y_i =\)</span> <strong>outcome</strong>/<strong>result</strong> so we want to minimize the the squares: <span class="math">\[\sum_{i=1}^n (Y_i - \mu)^2\]</span></li>
+<li>Let <span class="math">\(X_i =\)</span> <strong>regressor</strong>/<strong>predictor</strong>, and <span class="math">\(Y_i =\)</span> <strong>outcome</strong>/<strong>result</strong> so we want to minimize the the squares: <span class="math">\[\sum_{i=1}^n (Y_i - \mu)^2\]</span></li>
 <li>Proof is as follows <span class="math">\[
 \begin{aligned}
 \sum_{i=1}^n (Y_i - \mu)^2 &amp; = \sum_{i=1}^n (Y_i - \bar Y + \bar Y - \mu)^2 \Leftarrow \mbox{added}  \pm \bar Y \mbox{which is adding 0 to the original equation}\\