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Is a large standard error bad?
The Standard Error (“Std Err” or “SE”), is an indication of the reliability of the mean. A small SE is an indication that the sample mean is a more accurate reflection of the actual population mean. A larger sample size will normally result in a smaller SE (while SD is not directly affected by sample size).
What does a larger standard error mean?
The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.
What is considered a large standard error in regression?
A high standard error (relative to the coefficient) means either that 1) The coefficient is close to 0 or 2) The coefficient is not well estimated or some combination.
Are there large standard errors in logistic regression model?
There The result is that the sample size is vastly reduced to 240 and many coefficients and Standard Errors are large. Actually, that’s an understatement – the standard errors are very large indeed. Sorry to ask a simple question but do I have problems here? I would wager that something has actually went wrong.
When to use mixed effect logistic regression in data analysis?
Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and
Is it weird to get a very big odds ratio in logistic?
It is not a thing that can be answered technically – technically it is a large effect with wide confidence intervals but there looks to be an effect – the lower CI is 5- but does it make sense to you and who reads your papers that you are trying to publish – can you justify it.
How to adjust for non independence in logistic regression?
Logistic regression with clustered standard errors. These can adjust for non independence but does not allow for random effects. Probit regression with clustered standard errors. These can adjust for non independence but does not allow for random effects.