diff --git a/education/statistics/Correlation and Regression.md b/education/statistics/Correlation and Regression.md
index e9f7451..a29b604 100644
--- a/education/statistics/Correlation and Regression.md	
+++ b/education/statistics/Correlation and Regression.md	
@@ -113,9 +113,14 @@ $$ \sqrt{1-r^2}(\sigma_y) $$
 - On a least squared regression line, the 1 r.m.s error away will contain $2\sigma$ of the data, and it should loosely mirror a normal curve. 
 - To approximate the R.M.S error for a scatter diagram, take a high value and a low value for a given $x$ coordinate, and divide by 4, because r.m.s error is within $2\sigma$ of either side of the line. 
 - 68% = $2\sigma$, 95% = $4\sigma$
-- RMS can help determine which observations are outliers. Typically if a value is more than *2 r.m.s* away from the prediction estimate, it is considered to be an outlier.
+- RMS can help determine which observations are outliers. Typically if a value is more than *2 r.m.s* away from the prediction estimate, it is considered to be an outlier
+
+- Heteroscedastic scatter diagrams should not be used to make a prediction
+- homoscedastic scatter diagrams can be used to make predictions, because they follow
 ---
 # Terminology
-| Term | Definition |
-| -- | -- | 
-| $\hat{y}$ | The predicted value |
+| Term | Definition |  |
+| ---- | ---- | ---- |
+| $\hat{y}$ | The predicted value |  |
+|  Homoscedastic |  The scatter diagram will look the same above and below the LSRL |  |
+| Heteroscedastic | Will have more variability on one side of the regression line |  |