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Understanding the Role of R^2 in Regression Analysis

Uncover how R^2 enhances predictive accuracy in regression models compared to using the mean. Learn the significance of SSE differences and how they affect your projections, with a focus on interpreting R^2 values effectively.

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Understanding the Role of R^2 in Regression Analysis

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  1. Regression: How does r2 fit in? • How much better for predicting y is using regr line than just always using y ? • difference of SSE (sum of squares of errors) using avg [i.e., ∑(y -y)2] vs. SSE using regr [i.e., sum of squares of residuals ∑(y - ŷ)2], divided by SSE using avg ... • ... which = r2 (see next slide) • so, if r2 = 0.4, say, “regression results in a 40% improvement in projection”

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