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The coefficient of determination, also known as R² (R-squared), is a measure of the explanatory power of a regression model. It indicates how well the independent variable(s) explain the variation in the dependent variable. Here are some key points about the coefficient of determination:
Coefficient of Determination (R²): The coefficient of determination represents the proportion of the variance in the dependent variable explained by the independent variable(s) in the model. It ranges from 0 to 1, where 1 means the model explains all variations, and 0 means it explains none.
Interpretation: An R² of 0.75 would mean that 75% of the variation in the dependent variable can be explained by the independent variable(s) in the model.
Significance: A higher R² suggests that the model is better at explaining the variation in the dependent variable. However, it's important to consider other aspects of the model, such as residual analysis.
Limitations: R² alone does not provide information about causation or the validity of the model. A high R² does not necessarily imply causality.
The coefficient of determination is a useful tool in regression analysis, but it's crucial to consider it in the context of other evaluation criteria for a comprehensive assessment of the model.