Answer:
Explanation:
R^2, also known as the coefficient of determination, is a statistical measure that represents the ratio of the explained variation in the dependent variable Y (the model's predictions) compared to the total variation in Y.
In simple terms, R^2 quantifies how well the independent variables in a statistical model explain the variability observed in the dependent variable. It is a value between 0 and 1, where:
- R^2 = 0 means that the model does not explain any of the variability in Y, indicating a poor fit.
- R^2 = 1 means that the model perfectly explains all the variability in Y, indicating a perfect fit.
In practice, R^2 values between 0 and 1 are interpreted as follows:
- A higher R^2 value indicates that a larger proportion of the variation in Y is explained by the model, suggesting a better fit.
- A lower R^2 value suggests that the model does not explain much of the variation in Y, indicating a poorer fit.
R^2 is commonly used in regression analysis to assess the goodness of fit of a regression model and to understand how well the independent variables in the model predict the dependent variable.