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38, p > 0.05, we combined these data in the following analysis. In addition, a set of Shapiro?Wilktests(W) were conducted to test whether dependent variables deviated from normality. The examination of the normality of the distribution of cardiac RMSSD data (W = 0.96, p > 0.05) and of the iGDI metric (W = 0.97, p > 0.05) suggested that the obtained data was normally distributed. Therefore, a Pearson product-moment correlation coefficient was computed to assess the relationship between the % change in RMSSD and the eye-tracking metric measuring the divergence of eye-gaze patterns across subjects. 3. Results 3.1. Between-viewings prediction model results [http://www.selleckchem.com/VEGFR-PDGFR.html VEGFR2 cancer] Results of the between-viewings regression indicate that the iGDI explained 70% of the variance (R = 0.84, R2 = 0.701, F(1, 10) = 23.4, p  the [http://en.wikipedia.org/wiki/ZAP-70 ZAP-70] iGDI and the Ad-Meter scores; as the Ad-Meter score decreases, the iGDI increases, and vice versa. This negative correlation is as was expected because the Ad-Meter measures a positive attribute of the video (i.e., overall impact) whereas the iGDI was designed to capture a negative attribute of the video (i.e., its inability guide viewers attention). Figure 1 Eye-gaze Divergence (between-viewings) calculated on small sample is predictive of preference ratings in large audience. Vertical axis: subjective ratings for 2014 Super Bowl advertisements collected from a large online audience (USA Today Ad-Meter). ... Results on the generalizability of the between-viewings regression model show a small Root Mean Square Error (RMSE = 0.77) and Mean Absolute Percentage Error (MAPE = 10%). In addition, the cross validation coefficients across models show little variance across the different trained models (std(��1) = 1.1, std(��0) = 0.20) suggesting the stability of the regression-line parameters (slope and intercept). Moreover, the cross-validation coefficient is R2cv = 0.53 and the cross-validation shrinkage is Shrinkage = 0.17. 3.2. Within-viewings prediction model results Results of the within-viewings regression model indicate [http://www.selleckchem.com/products/ABT-869.html Linifanib manufacturer] that the iGDIv1 explained 54% of the variance (R = 0.73, R2 = 0.5385, F(1, 10) = 11.6708, p