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I expect you are talking about nominal categorical variables there? Ordinal variables with 100 levels are very strange. I have never seen a likert scale with 100 nuances or anything else that would wa …

If you want to have a model where 1 site = 1 observation, then you can use the weighting parameter of 'lm': lm(formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = …

This depends on how you compute the paired sample difference. before - after => greater after - before => less

When you use roc curves, you are saying that misclassification costs are not the same for different types of mistakes. If they were, you would just optimize classification accuracy which would be th …

So the issue with the decimal degrees of freedom is sorted out. When you use the basic version of the t-test, you get integer numbers that you can also find in tables. Let's put that aside. When you …

I'm mostly answering how and why the available data can be sufficient. This is what I understand your question to be about. Don't worry too much, you have the most important data available. I'm not s …

Your second quote says that confirmatory tests are not necessary. It states quite the opposite: One two sided test with level alpha is equivalent to doing two one sided tests with level alpha/2. So it …

Do you know the concrete costs of misclassification in both directions? I assume that missing the rare class must be much more costly than falsely adding a record to the rare class. (If not, then your …

I get to your main question in the second paragraph, first a more general remark: You probably already know that you should not be optimizing accuracy with a data-set that has this level of class imba …