Determining important factors in a regression
In the question i'm confused about it asks
"According to the estimated regressions, which of the seven factors Pod,pinst, pfive, oboard, fsch, D/v, pacq are "important" in explaining the value of a firm q(dependent variable)? as part of your answer explain in what sense they are important?
We are given a table of 9 different regressions, including a regression for each of the above factors listed by themselves along with the other coefficients. including the intercept and 4 other variables(that are mentioned in all the regressions).
We are also given all the t-statistics, for all factors in all the regressions and we are given an Rsquared value for each of the regressions.
We also have another regression that includes all these factors, and another one that includes one with pod^2?
Anybody know how i go about telling which of these is most important?
The Rsquared value are all very similar and only differ by 0.01, Some of the factors have a negative value and i know this means they have a negative correlation? But i'm not 100% sure if using these two things is right....
I read another example that says the fact that i'm missing variables from the regression could lead to simultaneous causality bias or omitted variable bias, so should i be comparing the regressions that only have one factor with the regression that includes all these factors??
Thanks in advance,
Re: Determining important factors in a regression
I think i posted this in the wrong thread....i posted in the advance statistics thread as well! Not sure how to delete it from here....