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Which "type" of residuals should I use when assessing autocorrelation in a binary logistic model?

In most presentations on discrete-time survival models (of which this is an example, with a subject-specific frailty modeled as a Gaussian random effect), there isn't much discussion of temporal ...
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Which assumptions do I need to check for a GLMM with a binary response (and how?)

In principle, a binomial can have all the problems any GLMM can have, in particular misfit distribution problems, including dispersion problems such as the four points you mention spatial / temporal /...
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Interpreting my residual plots

The plots indicate that the model suffers from heteroscedasticity. Generally, any systematic patterns (like a cone shape in the first plot or an inverted-U shape pattern in the second plot) may ...
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Martingale residuals in Cox PH model for categorical variable

I don't know that separate martingale residual plots for individual levels of an unordered categorical predictor are of much help. There isn't any linearity or functional form to evaluate among the ...
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5 votes

Understanding the assumptions of linear regression: residuals or data must come from a normal distribution?

First we should distinguish between errors and residuals. If $X_1,\ldots,X_n$ are sampled from a population whose average is $\mu,$ then $X_1-\mu,\ldots,X_n-\mu$ are the errors and $X_1-\overline X,\...
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7 votes
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Understanding the assumptions of linear regression: residuals or data must come from a normal distribution?

tl;dr: We need normal assumption for the standard $t$-test, $F$-test, and $CI$s. We make the assumption about how the errors are distributed randomly and then do all analysis on the distribution of ...
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Kruskal Wallis Test with Random Effect?

Apart from what Christian said in the comments: In a linear mixed model, you have 2 errors, the residual error and the random effect. In lme4, you can extract the residual error via residuals(model), ...
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What does a high residual error mean in regression model ( error value 65.89%)

This is an analysis of variance table. It tells you how much of the variance in your outcome variable can be explained by each of your predictors. In the first table, the the predictors together ...
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What does a high residual error mean in regression model ( error value 65.89%)

In terms of statistics, the contribution column tells you that the predictors flow rate, extrusion temperature and print speed explain more of the variability in UTS (81% + 4% + 1.6% = 86.6%) than the ...
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15 votes
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What is the reasoning behind expecting residuals in OLS regression to be normally distributed?

That author is writing nonsense. Just because errors are random doesn't mean that if you have a lot of them they will be normally distributed. It is absolutely not the case that OLS requires ...
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Using an independent variable correlated with the residuals in quantile regression

In principle yes - there is no difference between quantile and normal lm in that respect. Note, however, that omitted variable bias only occurs if you have collinearity among predictors. That means ...
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