brain freeze.....

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pad1w07@soton.ac.uk

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#1
im having a really stupid moment, but what does it mean when a multiple regression coeffecient is insignificant. say hair colour is found to be insignifcant to height (which u would expect) does this mean that hair colour doesnt influence, height, and more importantly its a bad predictor of height.

thanks
 
#2
If you refer to statistical significance, it implies that the predictor is not good enough. In order to be statistically significant the predictor has to be of a size that is more unexpected under the assumption that there is no effect than a criterion that you decide on beforehand (alpha-level). The size of the effect you can detect depends on various factors, e.g. sample size. The unstandardized coefficient gives you the expected change in the dependent variable given a 1 unit change in the predictor. Note that everything depends on which other predictors you have in the model. A predictor may be substantially related to the dependent on its own, but non-significant once other predictors are included in the model. This may be because the predictors overlap. The standardized coefficents represent semi-partial correlations, which in turn represent the correlation between X (predictor) and Y (dependent) after controlling for the effect of other included variables on X. So, if X is still a good enough predictor it means that it can account for variance in Y that none of the other variables can account for.