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The dataset description was vague, so it's a guess - but I suspect there is a bit of a Base Rate Fallacy/Survivorship Bias at play. It's easier to have a less money in your account, and therefore there's more people with a little or no money than people with a lot of money. On the other extreme, people with private swimming pools full of cash don't go ...


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In models with nonlinear link functions there is indeed a difference in the interpretation of the regression coefficients in GEEs and mixed-effects models. In short, GEEs give you the more usual interpretation of comparing groups of subjects. E.g., for dichotomous outcomes and the logit link you get the log-odds ratio between the group of males and the ...


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Multilevel models, without some additional tweaks, do not control for all possible between subject effects. For a given within-person (level 1) predictor's association with the outcome, you can control for unchanging between-person factors included in your model or not that might otherwise influence the association by including the level 1 variable's mean at ...


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This looks to me like a fairly standard case of overfitting, especially considering the amplitude of variations in loss seems to be going up with the number of epochs. Insofar as I can tell from the accuracy graph, the validation accuracy is increasing slightly. My suspicion then would be that your model has latched onto a reasonably-prevalent subset of ...


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There are a few things to discuss here: 1.) Your model is aiming to predict count data, this leads one to believe that you should be using either Poisson regression or Negative Binomial regression. However, this will depend on the distribution of your data. If the distribution of your outcome looks like either of the distributions in the following image: ...


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Regarding p-values: The p-values in the regression output are used to test the null hypothesis that the regression coefficient is 0, or in other words, that the variable is useful in predicting the response, given that the other variables are in the model. So the fact that the p-value for Time:Diet2 is greater than 0.05 means that you can conclude the ...


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"Difference of autocorrelation" doesn't make sense. Did you mean "autocorrelation of difference" ? You might want to look at http://www.autobox.com/pdfs/regvsbox-old.pdf as an introduction to "regression versus time series" suggesting a way to integrate both memory(arima) and regression (x) effects. You might want to find Mr. Chitra Baniya in Katmandu , ...


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You have two options: Option A) Try to figure out a confidence band for each observation in y, given its corresponding value of x (or x's, in case of multiple regressors).Keep on doing this independently for each observation, as if the other predictions did not exist, and you get your red-band. Note, the band has some non-zero width since the predicted ...


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