I just finished my intro stats class and wanted to revisit a VIX study I had posted a while ago. Needless to write, after the intro course I know only enough to run minitab but not enough to even hurt myself. In the last study, I had grouped percent changes in the VIX from the previous day to the current day and then I had looked at the magnitude of the percent changes in the dow 5 days out and 30 days out. In the last study, it seemed that the greater the magnitude of the vix changes, the greater would be the change in the dow over the next five days. Michael Cohn wrote me that basically I had discovered that the noise was normally distributed around the mean. At the time I didn?t understand that at all. However today I regressed the 1 day dow percent change onto the 1 day vix percent change and got exactly that, a good t test but an r-sq of ~0. I redid it using the absolute values of both just to see if the magnitude was at all predictive and got the following output: Predictor Coef SE Coef T P Constant 0.0067199 0.0001812 37.08 0.000 absolutevalue 0.008981 0.001836 4.89 0.000 S = 0.00714839 R-Sq = 0.6% R-Sq(adj) = 0.6% Analysis of Variance Source DF SS MS F P Regression 1 0.0012228 0.0012228 23.93 0.000 Residual Error 3666 0.1873307 0.0000511 Total 3667 0.1885535 The F is good. The SE is very low, but the R-sq is also very low. I wish I could post the scatterplot of %vix1daychange vs %dow1daychange. It?s a big circular blob, normally distributed around 0. Anyway, although I know very little about this stuff, I wanted to post a follow up.