What is considered a high standard error of 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.
What is considered to be a high standard error?
A high standard error shows that sample means are widely spread around the population mean—your sample may not closely represent your population. A low standard error shows that sample means are closely distributed around the population mean—your sample is representative of your population.
When do you have problems with logistic regression?
When the assumptions of logistic regression analysis are not met, we may have problems, such as biased coefficient estimates or very large standard errors for the logistic regression coefficients, and these problems may lead to invalid statistical inferences.
Why is there a huge standard error for a simple regression?
I was initially running a multinomial logistic regression, with multiple predictors. However, the standard error turned out to be huge for the parameters. So, I ran a simple logistic regression with just one predictor, but the standard error was still huge.
Why are there high standard errors in ml SE?
If in doubt and your data are all categorical, it can be appropriate and informative to check your results against an exact test or chi square. As for “high standard errors”, model ML SE is the reliability of parameter estimates based upon the data, not a measure of the reliability of your data per se.
Which is more useful standard error or are squared?
The standard error of the regression (S) is often more useful to know than the R-squared of the model because it provides us with actual units. If we’re interested in using a regression model to produce predictions, S can tell us very easily if a model is precise enough to use for prediction.