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Suppose you have a linear model where the model is significant but some coefficients are not. How does one interpret the model when some coefficients are not significant?

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  • $\begingroup$ You might have to explain a bit more about your model. Also, do you mean that some coefficients are significant whilst others not? $\endgroup$
    – Sootica
    Apr 8, 2013 at 9:45
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    $\begingroup$ This is normal situation. Similar question (you may ignore that the talk there is about stepwise selection) $\endgroup$
    – ttnphns
    Apr 8, 2013 at 12:58
  • $\begingroup$ Its a basic linear model with some coefficients significant at alpha = 0.05 and other coefficients not. $\endgroup$
    – phil12
    Apr 8, 2013 at 15:32
  • $\begingroup$ You may find the following related thread helpful as well: how can a regression be significant yet all predictors be non-significant?. $\endgroup$ Apr 9, 2013 at 21:30

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If collinearity is not a major problem, you interpret "non-significant" coefficients exactly the same as you interpret "significant" ones, with confidence intervals.

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This may be a sign of high collinearity among your predictors/covariates---if the overall or omnibus test is statistically significant but none of the individual covariates are significant. Check the condition indexes and their associated variance decomposition proportions.

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