Timeline for How to interpret the intercept term in a GLM?
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| Apr 15, 2015 at 14:54 | comment | added | Corvus | @Renee Nothing specific, but try en.wikipedia.org/wiki/Generalized_linear_model. | |
| Apr 15, 2015 at 14:48 | history | edited | Scortchi♦ | CC BY-SA 3.0 |
fixed typo
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| Apr 15, 2015 at 13:57 | comment | added | user73611 | @Corone - do you have a reference for this? Thanks in advance :) | |
| Oct 8, 2013 at 8:29 | history | edited | Corvus | CC BY-SA 3.0 |
typo
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| Mar 2, 2013 at 1:22 | comment | added | rolando2 | @Corone - I'm particulary interested in your comments here about variable in/exclusion and their relation to the thread at stats.stackexchange.com/questions/17624/… | |
| Jan 22, 2013 at 21:10 | comment | added | Corvus | @James - very good point, one should always report what variables you tested - I should have been clearer, I merely meant that one would typically not include that variable when trying to use the model to make a forecast (since it would usually mean overfitting). | |
| Jan 21, 2013 at 21:45 | comment | added | James Stanley | +1 for answer, (and suggestion in comment that something odd is happening in dataset) although I'd disagree with the opening of your comment "If a variables p-value is not small, the one would typically not include that variable in the model." This is not necessarily so -- often one wants to report the magnitude of a relationship, even if it is not "significant" (and more to the point, if you were interested in modelling a relationship to start with, then a null result is still important to report.) | |
| Jan 21, 2013 at 14:20 | comment | added | Corvus | If a variables p-value is not small, the one would typically not include that variable in the model. In your case the variable is not even being estimated to have a non-zero value, hence the p-value of 1.00. Basically there is no relationship between "treatment" and "attacked_excluding_app". The absence of relationship is so perfect here that it is almost suspcious, although you have a small dataset. It might be worth visualising your data, and seeing if it is reasonable. | |
| Jan 21, 2013 at 13:52 | comment | added | Samuel Waldron | You scared me at E[Y]=.... :). Thank you for the reply, I do (kind off) understand what you are saying. You said that the intercept is sig. non-zero, but the var. is not, it is p=1.00!? What effect does the variables p-value have on what I can say about the resut? | |
| Jan 21, 2013 at 13:02 | review | First posts | |||
| Jan 21, 2013 at 13:21 | |||||
| Jan 21, 2013 at 12:46 | history | answered | Corvus | CC BY-SA 3.0 |