Understanding R-Squared (R2) in Regression Analysis
Learn what R-squared (R2) means in regression analysis and how it measures model fit and explanatory power.
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R-squared (R2) is a statistical measure used in regression analysis to determine how well the relationship between two variables fits a linear model. Specifically, it quantifies the proportion of the variance in the dependent variable that is predictable from the independent variable(s). Essentially, R2 helps assess the accuracy and explanatory power of the model, with values ranging from 0 to 1, where higher values indicate a better fit.
FAQs & Answers
- What does an R-squared value of 0.8 mean? An R-squared value of 0.8 indicates that 80% of the variance in the dependent variable is predictable from the independent variable(s), suggesting a good fit.
- How do you interpret R2 in regression analysis? In regression analysis, R2 represents the proportion of variance in the dependent variable that can be explained by the independent variable(s), with a value closer to 1 indicating a stronger predictive capability.
- Can R-squared be negative? R-squared values are never negative when calculated correctly, as they range from 0 to 1. However, negative values can occur in the context of certain types of regression models, indicating a poor model fit.
- Why is R-squared important in statistics? R-squared is important because it provides insight into how well a statistical model explains the data, guiding data analysts in evaluating the effectiveness of predictive models.