Yes. Unless you have a large sample size, the contribution of x1 is in fact likely to be non-significant.
Basic question here but just want to double check my understanding..
When I do a multiple regression I get beta values b1=0.01 and b2=0.7
This basically means b2 has a larger effect than b1. Have I interpreted that correctly?
Yes. Unless you have a large sample size, the contribution of x1 is in fact likely to be non-significant.
jfw13 (11-27-2014)
I wouldn't be so quick to jump to conclusions. Scale of the independent variables plays a huge part in whether an effect is significant or not and whether or not it has a larger impact than another variable.
So my answer would be that it's not possible to tell and I would take Injektilo's statement that "Unless you have a large sample size, the contribution of x1 is in fact likely to be non-significant" with a grain of salt because you haven't told us anything about standard errors or sample sizes.
I don't have emotions and sometimes that makes me very sad.
Also, when you use the terminology "beta values" are you referring standardized or unstandardized regression weights...your language and notation will be confusing to some readers because you're using the language "beta" and the notation b1 and b2 - where "beta" implies "standardized" regression weights and the notation "b1 and b2" implies "unstandardized" regression weights.
In addition to what was said before, it would be interestingThis basically means b2 has a larger effect than b1. Have I interpreted that correctly?
to know whether x1 and x2 are (highly) correlated.
With kind regards
K.
it would also be interesting to know if x1 is acting as a suppressor for x2. it is well-known that suppressor variables can have very small (close to 0), non-significant regression coefficients yet contribute the largest to the explanatory power of the model when looking at the differences between R-squared.
i guess it's all just to say the size of a regression coefficient does not really tell you much in itself when its embedded within a larger regression model
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