brain freeze.....

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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.

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.