  # Jeremiahcp

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1. Make a histogram and check the relative frequency. If you have something that looks like a box, then there is equal probability, if not, then you can see which ones pop up more often in the long run. Assuming a 1000 games is enough to give you an accurate picture, most lotteries will have such a span of possible combinations 1000 games may not be good enough. So you could run some computer simulations to see just how well this approach works with your set up.
2. I think you would need to be more explicit in what you want to learn. If it is statistics I can make recommendations, but I have very little idea how the other sides do things. However, I think generally you'd want to make sure your algebra and calculus is top notch, then add in some basic understanding of proofs and linear algebra. At the very least.
3. ## Dependent variable bias? Strong correlation between baseline value and change over time.

R^2 is not a measure of correlation. It is a measure of variability in the dependent variable explained by changes in the independent variables. For example if you have an R^2 of 24% that means your regression model explains 24% of the variability in the dependent variable, which means you are missing 76% of the needed information. Now you can take the square root of R^2 to get Pearson's r, and that is an estimate of the correlation. But keep in mind that correlation is a very specific type of association, one that is linear in nature. However, you can still have non linear assoc
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