2.-Statistics/R-squared.md

@@ -135,14 +135,14 @@ $$
 
 ### Comparing Different Pseudo-R² Measures
 
-Each pseudo-R² provides an indication of the model fit, with values closer to 1 indicating a better fit. However, unlike traditional \(R^2\), pseudo-\(R^2\) values can vary depending on the type of model, and they don’t have a direct interpretation as the percentage of variance explained like traditional \(R^2\) does in linear regression models.
+Each pseudo-R² provides an indication of the model fit, with values closer to 1 indicating a better fit. However, unlike traditional $R^2$, pseudo-$R^2$ values can vary depending on the type of model, and they don’t have a direct interpretation as the percentage of variance explained like traditional $R^2$ does in linear regression models.
 
-- **McFadden’s pseudo-\(R^2\)**: Tends to produce values lower than traditional \(R^2\), and values between 0.2 and 0.4 are considered good fits in many contexts.
+- **McFadden’s pseudo-$R^2$**: Tends to produce values lower than traditional $R^2$, and values between 0.2 and 0.4 are considered good fits in many contexts.
   
-- **Cox & Snell’s pseudo-\(R^2\)**: Provides a likelihood-based measure but does not reach 1, making interpretation less straightforward.
+- **Cox & Snell’s pseudo-$R^2$**: Provides a likelihood-based measure but does not reach 1, making interpretation less straightforward.
 
-- **Nagelkerke’s pseudo-\(R^2\)**: Adjusts Cox & Snell’s measure to allow for a maximum value of 1, making it easier to interpret, but still not as intuitive as traditional \(R^2\).
+- **Nagelkerke’s pseudo-$R^2$**: Adjusts Cox & Snell’s measure to allow for a maximum value of 1, making it easier to interpret, but still not as intuitive as traditional $R^2$.
 
 ### Interpreting Pseudo-R² Measures
 
-When comparing models using pseudo-\(R^2\), it's important to note that these measures are relative within the context of a specific model. Unlike traditional \(R^2\), they are not directly comparable across different types of models or datasets. As such, while pseudo-\(R^2\) values can offer insight into model performance, they should be interpreted with caution and supplemented with other evaluation metrics, such as likelihoods or classification accuracy, depending on the model type.
+When comparing models using pseudo-$R^2$, it's important to note that these measures are relative within the context of a specific model. Unlike traditional $R^2$, they are not directly comparable across different types of models or datasets. As such, while pseudo-$R^2$ values can offer insight into model performance, they should be interpreted with caution and supplemented with other evaluation metrics, such as likelihoods or classification accuracy, depending on the model type.