2.-Statistics/R-squared.md

@@ -23,13 +23,13 @@ R^2 = 1 - \frac{SS_{\text{residual}}}{SS_{\text{total}}}
 $$
 
 Where:
-- **SS_residual** is the sum of squared differences between the observed values \(y_i\) and the values predicted by the model \( \hat{y}_i \) (i.e., the residuals).
+- **SS_residual** is the sum of squared differences between the observed values $y_i$ and the values predicted by the model $\hat{y}_i$ (i.e., the residuals).
 
 $$
 SS_{\text{residual}} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2
 $$
 
-- **SS_total** is the total sum of squared differences between the observed values \(y_i\) and the mean of the response variable \( \bar{y} \).
+- **SS_total** is the total sum of squared differences between the observed values $y_i$ and the mean of the response variable $\bar{y}$.
 
 $$
 SS_{\text{total}} = \sum_{i=1}^{n} (y_i - \bar{y})^2