colinearity between variables I am running a multiple regression of Y~a+b+c+d etc...  
I want to do a quick check to see whether my different explanatory variables are colinear (they're a mix of categorical and continuous).  There seems to be a whelm of complicated statistics behind all this -- would looking at the R-squared value of a simple regression between each variable in turn (a~b, a~c, a~d etc) be a satisfactory coarse estimate of colinearity? Or would I have to use VIF statistics and other more complicated methods?
I hope that this is not off-topic... :-) 
Edit: Also, if a simple look at the R-squared value is sufficient, what is an acceptable amount of correlation?  I have two variables that are 54% correlated for example - this seems high to me...
 A: $R^2$ does participate in VIF calculation, but it's not the $R^2$ from the model that involves $y$ but the  $R^2$ among the independent variables.
Given a regression mode:
$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3,$
the VIF of the first predictor, $VIF_{x_1}$, is:
$VIF_{x_1} = \frac{1}{(1-R^2_{x1})},$
where $R^2_{x1}$ is the $R^2$ of the regression model:
$x_1 = \gamma_0 + \gamma_1 x_2 + \gamma_2 x_3$
As you can see, if $x_2$ and $x_1$ are highly collinear, then $R^2_{x1}$ will be very high, causing the VIF to be very high as well.
Your method of checking all possible pair-wise correlations among predictors is close, but it does not incorporate the scenario in which more than two predictors are collinear. For instance, if you fit percents of energy from dietary fat, protein, and carbohydrate into the model, multiple correlation test will not find it out, but VIF will pick it up. So, use your method for exploratory purpose and know its limitation.
If you talk about just a pair of continuous predictors having a very high VIF, they usually should be in the vicinity of |r| > 0.9 in order to cause VIF to be bigger than 6, which is a conventional threshold beyond which some investigation should be merited.
