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R is using "LocationMonmouth" and "TreatmentCC" as reference levels and the other levels are given as relative to that. By way of example let's consider a simple model that only has the location variable in it: What you want to see is: $yield = intercept + Monmouth*IndicatorMonmouth + Urbana*IndicatorUrbana$ Where $IndicatorMonmouth$ is 1 if the data ...


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Firstly, this is a very strange model --- it is a binomial GLM with an identity link function, which is a strange link function to choose in this context. In any case, even taking that model as fixed, the explanation in the book seems quite strange to me. Re-arranging the regression equation, and taking the variance of both sides (treating the explanatory ...


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There are a couple of ways the logistic regression model can be rewritten. Let $X$ denote the student's GPA. The standard way the model is expressed is in terms of the log odds. We have $$\text{log}\left(\frac{\pi}{1-\pi}\right)=\alpha+5.45X$$ and so a one unit increase in $X$ would increase the expected log odds by 5.45. Exponentiating both sides we get ...


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If every disease is either chronic or persistent, then you can remove the disease variable. If there are other ways to have a disease, then you can have diseasechronic, diseasepersistent and diseaseother for each. This will avoid collinearity; it becomes a little more complex to look at the overall effet of disease.


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I use the repeats as I have a small dataset (<200) and would like tighter bounds on my model performance for significance testing. Repetitions of $k$-fold cross validation allow you to measure model instability, and the uncertainty related to that source of variance will go down if you average repetitions. But it doesn't do anything about the actual ...


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