How does bagging help reduce the variance
High Variance  - Model varies a lot on small changes
High Bias - Model doesn't vary so much but predict quite away from the truth

Let's check a Decision Tree on 5 values - 
\begin{array} {|r|r|}
\hline
1 &amp;5 &amp;10 &amp;15 &amp;20\\
\hline
\end{array}
In this tree split, 
Value of 9.9 will be 7.5 
Value of 10.1 will be 12.5. 
Showing a very high variance.

Let's create 4 Random Tree of 3 elements each - 
\begin{array} {|r|r|}
\hline
Tree-1 &amp;5 &amp;10 &amp;15\\
\hline
Tree-2 &amp;1 &amp;15 &amp;20\\
\hline
Tree-3 &amp;1 &amp;05 &amp;20\\
\hline
Tree-4 &amp;5 &amp;15 &amp;20\\
\hline
\end{array}
Value of 9.9 = (7.5 + 7.5 + 12.5 + 10)/4 ~ 9.375
Value of 10.1 = (12.5 + 7.5 + 12.5 + 10)/4 ~ 10.625
Variance is reduced a lot. 
In bagging, we build multi-hundreds of the Tree(Can build other models too which offers high variance) which results in a large variance reduction