The original form of the given expression is:

$R^2 = \frac{\text{Explained Variation}}{\text{Total Variation}}$

This represents the coefficient of determination, often used in statistical models to measure the proportion of variance in the dependent variable that is explained by the independent variables.

Would you like further details or have any questions about this?

Here are 5 related questions for further exploration:

1. How is the coefficient of determination $R^2$ interpreted in regression analysis?
2. What is the difference between explained and unexplained variation in statistical models?
3. How does $R^2$ relate to model accuracy and goodness-of-fit?
4. In what situations can the value of $R^2$ be negative?
5. How does $R^2$ differ from the adjusted $R^2$?

Tip: A high $R^2$ value indicates that the model explains a large portion of the variance, but it doesn't always mean the model is the best fit for prediction.