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Why are my standard errors so high?
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.
What does a probit model do?
Probit models are used in regression analysis. A probit model (also called probit regression), is a way to perform regression for binary outcome variables. Binary outcome variables are dependent variables with two possibilities, like yes/no, positive test result/negative test result or single/not single.
Which of the following is correct concerning logit and probit models?
Response a is correct since the logit and probit models are similar in spirit: they both use a transformation of the model so that the estimated probabilities are bounded between zero and one – the only difference is the form of the transformation – a cumulative logistic for the logit model and a cumulative normal for …
Is a high standard error bad?
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 standard error for mean ED50 in probit?
The 0.5 is for EC50 or ED50 calculation, as yours (I guess ) People use EC90 (0.9) or LD10 (0.1) etc depending on context. I am using specifically to calculate LLOD (lower limit of detection, for molecular detection assays) at probit 0.95.
What is the use of robust standard errors?
The “robust” standard errors are being reported to cover the possibility that the model’s errors may be heteroskedastic. But if that’s the case, the parameter estimates are inconsistent. What use is a consistent standard error when the point estimate is inconsistent?
What does large standard error of intercept-Statalist mean?
When we run a regression (e.g. ordinary least squares, or probit) with an intercept, if our estimate of the intercept has a very large standard error, does it say anything bad about the model? Does it have something to do with the skewness of the data?
Can a standard error be used as a predictor?
Using (year – 2000) or (year – 1960) as predictor or whatever gives the intercept more interest as the rate in 2000, 1960 or whatever, and standard errors can also be taken more seriously. This seems to be something that every experienced data analyst recognises as a standard trick but which is rarely written up in texts.