Math.md

 # Linear Regression
 
-This document contains information regarding the Odinary Least Squares linear regression implemented in the evaluation tool of the visualtisation platform.
+This document contains information regarding the Ordinary Least Squares linear regression implemented in the evaluation tool of the visualisation platform.
 
 ## Model
 
@@ -36,7 +36,7 @@ with _E_(ε) = 0, it becomes :
 and we assumed normality so Var(&epsilon;) = _E_(&epsilon;&epsilon;') = &sigma;<sup>2</sup>_I_, where &sigma;<sup>2</sup> > 0 is the common variance/error of each element in the vector of errors or the mean-squared error. This gives us:
 * Var(&beta;<sup>_e_</sup>) = (__X'X__)<sup>-1</sup>__X'__ &sigma;<sup>2</sup>_I_ __X__(__X'X__)<sup>-1</sup>
 
-simplifing this, we get the error or variance for each coefficient given by
+simplifying this, we get the error or variance for each coefficient given by
 
 * Var(&beta;<sup>_e_</sup>) = &sigma;<sup>2</sup>(__X'X__)<sup>-1</sup>