Timeline for My p-values increase when adding variables: is the model still valid?
Current License: CC BY-SA 3.0
15 events
| when toggle format | what | by | license | comment | |
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| Feb 21, 2015 at 10:54 | answer | added | Nick Cox | timeline score: 5 | |
| Feb 20, 2015 at 21:47 | comment | added | gung - Reinstate Monica | @NickCox, I think that is the right answer here. Why not make it 'official'? | |
| Feb 20, 2015 at 20:30 | comment | added | Sensed | Are we talking about the same issue? Introducing my new variable doesn't make an "insignificant" variable significant. But the other way around, adding a variable makes a previously signifcant variable insignificant (While the regression as a whole still being significant. Or, or, or is it the same difference? - Thanks | |
| Feb 20, 2015 at 18:16 | comment | added | whuber♦ | Yes, this is a collinearity issue. But strong collinearity is not actually needed: see the example I presented at stats.stackexchange.com/a/14528, for instance. Collinearity is not needed at all to explain the reverse effect (when introducing a new variable suddenly makes an "insignificant" variable significant): stats.stackexchange.com/a/28493. You should both (a) study "stepwise regression" (which is what you're trying to do) and (b) search our site to learn why you should look for better procedures. | |
| Feb 20, 2015 at 17:13 | answer | added | Alexis | timeline score: 10 | |
| Feb 20, 2015 at 16:55 | comment | added | Sensed | Thanks, I read though some of these and added an edit to my original post. | |
| Feb 20, 2015 at 16:54 | history | edited | Sensed | CC BY-SA 3.0 |
I did a VIF Test, still not understanding...
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| Feb 20, 2015 at 14:50 | comment | added | whuber♦ | This is a FAQ: please take a look at our higher-voted threads on regression and significance. | |
| Feb 20, 2015 at 14:33 | history | edited | Sensed | CC BY-SA 3.0 |
More on my process
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| Feb 19, 2015 at 22:10 | history | edited | Nick Cox | CC BY-SA 3.0 |
edited body
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| Feb 19, 2015 at 22:07 | comment | added | Nick Cox | If your sample size is 9, then multiple regression is a real strain. Examples could be imagined in which it works fine, but usually the experience will be similar to yours. The P-values are a health warning that you do not have a sample size large enough to do what you are trying. As well as being very cautious, don't fit anything that you can't support graphically, which may well mean trying at most two predictors. (Using a model just to predict makes no difference here; an untrustworthy model is untrustworthy regardless of your motive.) | |
| Feb 19, 2015 at 22:07 | answer | added | bdeonovic | timeline score: 1 | |
| Feb 19, 2015 at 22:03 | history | edited | Nick Cox | CC BY-SA 3.0 |
deleted 4 characters in body
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| Feb 19, 2015 at 22:02 | review | First posts | |||
| Feb 19, 2015 at 22:03 | |||||
| Feb 19, 2015 at 21:57 | history | asked | Sensed | CC BY-SA 3.0 |