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1. Probability of staff exiting a company

If that's the only info you have then you would probably need to make some sort of independence assumption. It's probably not reasonable if this is an actual real life problem though.
2. Minimum R2 in Linear regression

And there are fields where an R^2 of 0.2 is groundbreaking
3. Why is chance of a Type 1 error equal to alpha?

In the standard hypothesis testing framework you can't and don't make that conclusion.
4. Free Range Logic

AND OR min(max( did, they))?
5. Free Range Logic

It looks like you're just giving a different name to an already established idea: Fuzzy Logic
6. Std Dev Bounds

I don't know how you did the subtraction but the mean you provided minus the standard deviation you provided doesn't result in what you said you got.
7. puzzle

I was thinking the same thing. If we have a positive integer restriction then there is a unique solution but otherwise no.
8. Should I consider this variable continuous or discrete?

Discrete vs continuous typically doesn't matter when it comes to models outside of choosing a theoretical conditional distribution for the dependent variable. Also possibly deciding if it makes sense to treat a numeric predictor as categorical but that's more in number of levels and discrete vs...
9. puzzle

Without writing anything on paper I don't think there is a unique a solution. I think we can define the possible values though.
10. ؟؟

If you assume a normal distribution then look up the empirical rule. If not then search Chebyshevs inequality
11. Delete Excess Data and Not Lose Stat Significance?

If the main concern is tracking all time counts then would it be possible to just have two different views of the data. Certainly you need to track each individual song with an entry in your database regardless so why not have an additional column for total all time play count as well. Any time...
12. Convergence of MCMC chains

They're only presenting the last 200 iterations of a 95k chain
13. Difference between simulating the dependent variable and simulating the error terms and adding them to the fitted values values assuming normality?

tmp <- predict(mod) + rnorm(length(predict(mod)), 0, summary(mod)\$sigma) This takes the fitted values from your model and then simulates new error terms for them based on the estimated error variance. This is typically what is known as a parametric bootstrap. tmp <-...
14. Set Level Prediction in Tennis

For the soccer model we used things like overall team salary, last ten games number of wins, certain Vegas odds, etc, ...as external variables to help with the predictions.
15. Set Level Prediction in Tennis

A fairly simplistic but surprisingly good place to start might be a Bradley Terry model. https://en.m.wikipedia.org/wiki/Bradley%E2%80%93Terry_model I helped somebody with predicting soccer games and it worked better than expected We did incorporate external predictors to modify the...
16. Transform for one distribution to another

I mean I outlined that procedure already. You need the CDF and Inverse CDF but with some math you can get that from a PDF.
17. Transform for one distribution to another

I guess I'm not sure why you want to do this. Some details on that would be helpful in determining if what you're doing makes any sense or not or if there is something better you could do. With that said... So it's almost always possible to transform one continuous distribution to a different...
18. [Excel] Help Requested - Bayes' Theorem posterior odds > 1

Can you post your numbers that you used to get that result. Like actually post your calculation - not just the formula used in the spreadsheet
19. Help with negative exponent in computing variable?

You have your parenthesis wrong. You want something like: -(-13.221 + (PSS1 * 1.890) + (PSS2 * 1.002) + (PSS3 * 1.238) + (PSS4 * 2.172)) Hopefully that will bring your values into the realm of the reasonable. Edited: Just to fill out the rest of the values and provide some additional clarity

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