fats_tromino

I agree with this as well. I tried using Duckduckgo around a year and a half ago or two years ago and abandoned it due to poor search results, but I began using it again a couple months ago and it seemed much improved.

This is the most succinct and clearest explanation of what's going on. I see this discussed a lot when people talk about the curse of dimensionality. Another very simple example is the example of a n-hypercube with edge length 1/2 embedded in the unit n-hypercube. As n increases, the volume of the unit hypercube is constant (1), whereas the volume of the smaller hypercube is decreasing at an exponential rate.

Thanks for the clarification about Mallow's C_p, I always get the name messed up for some reason.

Thank you, I'm confused as to why this is happening.

Also some features of regression and hypothesis testing are actually fairly robust to non-normality, particularly with large sample sizes.

Look at normal qq-plot to measure normality

Just as two quick comments, confidence intervals != prediction intervals. One gives a range around a true mean value, the other gives a range around where the variable may actually fall (prediction intervals are bigger).

You may also want to mention adjusted R^2 as a measure of quality of the model. I've never heard of AICR before, the standard metrics for quality of the model are AIC/BIC (and sometimes Mallus Cp).

Edit: fixed sloppy wording about confidence interval.