(07-07-2017, 03:51 PM)timeconsumer Wrote:I'm a big fan of the regression analysis. The output will give you the r-squared value which tells you how much variance in the dependent variable can be explained by the independent variable(s).
Then you can check your ANOVA table underneath that to see the Significance F value to check the statistical significance of the whole test (and the associated r2).
Then finally your coefficients table gives you the p-value for each attribute while also showing you a coefficient attempting to explain what an increase of 1 in x does to y.
But here's the big issue with a pearson correlation and a regression (and most other tests we would use as amateurs) they rely on continuous data. And with my understanding of this sim attributes and their affect on the player are not continuous. This means that going from 70 to 80 strength does not give as much of a benefit as going from 90 to 100 strength. So instead we have to weight our attributes to account for this. I have a method of weighting that I use, and it's working okay for me, but unfortunately it's secret Otter property. Play around with weighting it, you might get a better system than me.
This is super helpful information. I'll probably do a follow up article that shows all of the summary outputs and what it means once I figure that out.
However, with how I did the first article, that's 7 regression tests for each stat just to see how it effects tackling or sacking or 1 other aspect. This is going to be a lot of stuff to go through but pretty cool to look at. I definitely get what you mean with the weighted stats, I'm looking forward to playing around with that.
Edit: Disregard some of my post, For some reason I didn't think I could do a multivariable regression and would have to look at them individually.
