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IMHO the main issue with this kind of analysis is that you can't usually get a lot of explanatory power from simple regressions.League is a complex game with lots of interactions* (so simple models will explain little), as well as meta changes that make acquiring consistent large samples difficult (so complex models will be difficult to reliably estimate).*And, specifically, suggests your data is non-linear, rather than a "goodness of fit" explanation.
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usp=sharing Data Set: https://docs.google.com/spreadsheets/d/1pnw6e_6xlr Fel Jz DM5F36VIMROS9n Rhccs-1d6c-2KE/edit? I did undergrad economics followed by a masters in finance with a lot of statistics in there.
I've also been in the situation when writing a thesis where the data doesn't work out like I hoped it would but you have to adapt to it and identify what you can say given the data you have.
Feel free to use the data for what you will, just make sure to cite me.
Paper: https://drive.google.com/file/d/0Bx Yrdvi3ABc BV2x5Wm VFej Jh ZDg/view?
When people explore through Elixir.com, they often approach me with lots of ideas of different kinds of analyses to run, relationships they could construct between different variables, but Lo L is far too complicated an environment to do what baseball and other traditional sports have done with their stats. I remember something like heteroskedasticity not necessarily influencing parameter values, but their error estimates (i.e.
your slope and intercept are OK but your error estimates, CIs etc are off), but I don't trust my memory on something I haven't touched for some years.
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