Re: ols regressions

On Oct 22, 4:54 am, joseph Frank <josephFrank1...@xxxxxxxxxxx> wrote:
I have no multicollinearity problem as the correlation matric shows low correlation between the variables. I do agree that putting a threshold of 2.5% and 5% would eliminate non-outliers. But what troubled me about his comment is that to eliminate the X outliers. Is it normal to look at the outliers of the independent variable?

Here is a sample data set that Jerry Dallal posted a few years ago
during a discussion of multicollinearity.

X1 X2 X3 Y
18 88 106 13
72 45 117 43
36 63 99 50
75 26 101 77
22 83 105 23
99 71 170 68
69 53 122 6
6 49 55 51
86 99 185 37
85 64 149 10
87 7 94 32
93 32 125 69
44 88 132 4
34 34 68 13
84 28 112 18

Look at the correlation matrix for X1-X3. The values of r range from
-.307 to .657--values will probably not raise any red flags. Then
regress Y on X1, X2 and X3. Notice that one of the 3 variables is
excluded from the equation, because tolerance = 0.

On the flip-side, bivariate correlations can be quite high without
signaling any problematic multicollinearity (e.g., when polynomial
terms are included in the model).

So, you can have complete linear dependence despite the absence of any
alarming bivariate correlations, and you can have no problematic
multicollinearity when one or more of the correlations does look
high. Therefore, using the correlation matrix to diagnose
multicollinearity is not a good idea.

--
Bruce Weaver
bweaver@xxxxxxxxxxxx
http://sites.google.com/a/lakeheadu.ca/bweaver/Home
"When all else fails, RTFM."
.