# ROI results # ROI: 500 - R. Insula Call: lm(formula = "ROI_500 ~ Age+Sex", data = roidataframe) Coefficients: (Intercept) Age SexM 7.94917 -0.04644 -1.42289 Call: lm(formula = "ROI_500 ~ Age+Sex", data = roidataframe) Residuals: Min 1Q Median 3Q Max -2.5180 -0.5975 -0.4345 0.3109 7.5448 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.94917 1.22168 6.507 1.52e-06 *** Age -0.04644 0.02815 -1.650 0.1132 SexM -1.42289 0.75667 -1.880 0.0734 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.837 on 22 degrees of freedom Multiple R-squared: 0.2008, Adjusted R-squared: 0.1281 F-statistic: 2.763 on 2 and 22 DF, p-value: 0.08501 Call: lm(formula = "ROI_500 ~ Sex", data = roidataframe) Coefficients: (Intercept) SexM 6.092 -1.256 Call: lm(formula = "ROI_500 ~ Sex", data = roidataframe) Residuals: Min 1Q Median 3Q Max -1.7286 -0.8787 -0.2423 0.1246 8.3806 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.0917 0.4917 12.390 1.17e-11 *** SexM -1.2557 0.7774 -1.615 0.12 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.904 on 23 degrees of freedom Multiple R-squared: 0.1019, Adjusted R-squared: 0.06283 F-statistic: 2.609 on 1 and 23 DF, p-value: 0.1199 Analysis of Variance Table Model 1: ROI_500 ~ Age + Sex Model 2: ROI_500 ~ Sex Res.Df RSS Df Sum of Sq F Pr(>F) 1 22 74.221 2 23 83.403 -1 -9.1824 2.7218 0.1132 Analysis of Variance Table Model 1: ROI_500 ~ Age + Sex Model 2: ROI_500 ~ Sex Res.Df RSS Df Sum of Sq F Pr(>F) 1 22 74.221 2 23 83.403 -1 -9.1824 2.7218 0.1132 ##__________________________________________________________ # ROI: 501 - L. Insula Call: lm(formula = "ROI_501 ~ Age+Sex", data = roidataframe) Coefficients: (Intercept) Age SexM 7.32012 -0.03601 -0.96903 Call: lm(formula = "ROI_501 ~ Age+Sex", data = roidataframe) Residuals: Min 1Q Median 3Q Max -1.9624 -1.1582 -0.2743 0.1830 8.4355 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.32012 1.38179 5.298 2.57e-05 *** Age -0.03601 0.03184 -1.131 0.27 SexM -0.96903 0.85584 -1.132 0.27 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.077 on 22 degrees of freedom Multiple R-squared: 0.09312, Adjusted R-squared: 0.01068 F-statistic: 1.13 on 2 and 22 DF, p-value: 0.3412 Call: lm(formula = "ROI_501 ~ Sex", data = roidataframe) Coefficients: (Intercept) SexM 5.8797 -0.8394 Call: lm(formula = "ROI_501 ~ Sex", data = roidataframe) Residuals: Min 1Q Median 3Q Max -1.7103 -0.8739 -0.4494 0.2561 9.0837 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.8797 0.5397 10.895 1.48e-10 *** SexM -0.8394 0.8533 -0.984 0.335 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.09 on 23 degrees of freedom Multiple R-squared: 0.04038, Adjusted R-squared: -0.001347 F-statistic: 0.9677 on 1 and 23 DF, p-value: 0.3355 Analysis of Variance Table Model 1: ROI_501 ~ Age + Sex Model 2: ROI_501 ~ Sex Res.Df RSS Df Sum of Sq F Pr(>F) 1 22 94.951 2 23 100.473 -1 -5.5224 1.2795 0.2702 Analysis of Variance Table Model 1: ROI_501 ~ Age + Sex Model 2: ROI_501 ~ Sex Res.Df RSS Df Sum of Sq F Pr(>F) 1 22 94.951 2 23 100.473 -1 -5.5224 1.2795 0.2702 ##__________________________________________________________