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Table 1. 
Stepwise Linear Regression of DA on CFE and DA
Two-Step Multiple Regression of DA on K and CFE
Model PredictorsModel SummaryPredictor Summary
Fdfprrstprs
Filtering alone 
 Filtering (CFE)       .43 .43 2.48 .019 
  
Capacity alone 
 Capacity (K      .31 .31 1.73 .095 
  
Capacity and filtering 3.317 2, 27 .05         
 Capacity (K      .31 .13 0.76 .46 
 Filtering (CFE)       .43 .32 1.84 .076 
Two-Step Multiple Regression of DA on K and CFE
Model PredictorsModel SummaryPredictor Summary
Fdfprrstprs
Filtering alone 
 Filtering (CFE)       .43 .43 2.48 .019 
  
Capacity alone 
 Capacity (K      .31 .31 1.73 .095 
  
Capacity and filtering 3.317 2, 27 .05         
 Capacity (K      .31 .13 0.76 .46 
 Filtering (CFE)       .43 .32 1.84 .076 

Pearson correlation coefficients and their associated t test of the correlation coefficient against 0 are given for the models of each predictor alone. The omnibus F test of the full model is provided for the model including both predictors, as well as t tests of their associated partial correlation coefficients (rp). Omnibus F tests are not provided for single predictors, because these are equivalent to the t test of the correlation coefficient against zero. Critically, the regression model of DA on both CFE and VWM capacity shows that the covariance between DA and VWM capacity is largely accounted for by CFE, which remains trending toward significance as a predictor of DA despite the addition of VWM capacity to the model.

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