ࡱ> 352'` +bjbj{P{P 4R::hhhhhhh|''''48'|6((((((((d6f6f6f6f6f6f67h=:xf6Eh+((++f6hh((6h-h-h-+h(h(d6h-+d6h-h-hhh-(' w',.h-2|606h-:4,:h-:hh-()h-*+(((f6f6R-(((6++++||| ||||||hhhhhh  R2 Explained and Illustrated 1. R2 Defined Recall that R2 is a measure of the proportion of variability the DV that is predicted by the model IVs. R2 is the change in R2 values from one model to another. R2 is the incremental increase in the model R2 resulting from the addition of a predictor, or set of predictors, to the regression equation. 2. Example Model 1 (Reduced model) Test Scores = b0 + b1 (IQ) + e DV = Student Reading Test Scores IV 1 = IQ Model 2 (Full model) Test Scores = b0 + b1 (IQ) + b2 (Study Time) + e DV = Student Reading Test Scores IV 1 = IQ IV 2 = Amount of time spent studying before test ModelsR2 Full:Test Scores = b0 + b1 (IQ) + b2 (Study Time) + e .80Reduced:Test Scores = b0 + b1 (IQ) + e .60Change in R2 values = R2 (Study Time) = .80 - .60 =.20 3. 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