# Differences

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 example:effectsize [2018/10/21 15:00]42.49.180.224 [Computing and using estimates of effect size] example:effectsize [2017/10/09 17:07] (current)robert added tags 2018/10/22 13:25 robert old revision restored2018/10/21 15:00 [Computing and using estimates of effect size]2017/10/09 17:07 robert added tags2017/10/09 09:50 robert 2017/10/09 09:49 robert 2017/10/09 09:47 robert 2017/10/09 09:46 robert created 2018/10/22 13:25 robert old revision restored2018/10/21 15:00 [Computing and using estimates of effect size]2017/10/09 17:07 robert added tags2017/10/09 09:50 robert 2017/10/09 09:49 robert 2017/10/09 09:47 robert 2017/10/09 09:46 robert created Both sides next revision Line 5: Line 5: The following code demonstrates how you can compute and plot the effect size. The following code demonstrates how you can compute and plot the effect size. - <code> + % find the interesting segments of data % find the interesting segments of data cfg = []; cfg = []; Line 89: Line 89: % Huge        2.00 % Huge        2.00 - </code> + It is interesting to see how the effect size increases by taking the average over more channels and time points. It is interesting to see how the effect size increases by taking the average over more channels and time points. - <code> + %% %% Line 101: Line 101: % now repeat the computation of Cohen'​s d above % now repeat the computation of Cohen'​s d above - </code> + Proper preprocessing of the data also increases the effect size. Proper preprocessing of the data also increases the effect size. - <code> + %% %% Line 120: Line 120: h2 = histcounts(x2,​edges);​ h2 = histcounts(x2,​edges);​ bar(edges(1:​end-1),​[h1;​ h2]'); legend({'​FC',​ '​FIC'​}) bar(edges(1:​end-1),​[h1;​ h2]'); legend({'​FC',​ '​FIC'​}) - </code> +