Hello
1) In logistic regression, if confidence interval includes the value 0 then what will be the conclusion on significance of the variable.
2) How to decide whether the data is Linear or Linearly Separable?.
Thanks
@KafeelBasha wrote:
1) Is there any specific reason behind saying a variable is insignificant if the confidence interval includes 0.
2) I came across below warning message while going through an example of logistic regression in R
1) Because if it includes 0 in the interval that means a valid value is 0, which is no difference, ie not significant difference. I would suggest re-reading how confidence interval, p-values and hypothesis testing all relate.
2) Ask R questions in an R forum, ask SAS question in a SAS forum. But it means that some variables in your data are essentially the same. For example, a very basic examples is if you had two variables that are X=1 if A is present, 0 otherwise and Y=1 if A is not present, otherwise Y=0. They're the same.
1) it is not significant.
2)don't understand your question. You mean linear or nonlinear relationship between X and Y ?
Check EFFECT statement.
Or you should check Q-Q plot .
1) Is there any specific reason behind saying a variable is insignificant if the confidence interval includes 0.
2) I came across below warning message while going through an example of logistic regression in R
http://michael.hahsler.net/SMU/EMIS7332/R/logistic_regression.html
When can we say the data is linear or Linearly Separable?
Thanks in advance.
@KafeelBasha wrote:
1) Is there any specific reason behind saying a variable is insignificant if the confidence interval includes 0.
2) I came across below warning message while going through an example of logistic regression in R
1) Because if it includes 0 in the interval that means a valid value is 0, which is no difference, ie not significant difference. I would suggest re-reading how confidence interval, p-values and hypothesis testing all relate.
2) Ask R questions in an R forum, ask SAS question in a SAS forum. But it means that some variables in your data are essentially the same. For example, a very basic examples is if you had two variables that are X=1 if A is present, 0 otherwise and Y=1 if A is not present, otherwise Y=0. They're the same.
H0: b=0 , so if b -> [-x,x ] then it can not indicate if b=0 or not . "probability numerically 0 or 1" It is more like SAS 's message "can't separate likelihood". Maybe your CLASS variable have some level which is dropout. E.X. sex=F appeared only once . Use proc freq; table sex;run; could explore more information about CLASS variables.
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