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Re: st: tobit?
I agree with Kieran. There was a thread about using ratios as
dependent or predictor variables in June, 2007, "dependent as
denominator on the RHS". Here is part of my response, which is
missing from the archive.
Dick Kronmal (RA Kronmal, 1993. Spurious Correlation and the Fallacy
of the Ratio Standard Revisited. Journal of the Royal Statistical
Society A, 156, 379-392) treated different problems from the one
asked about. The following is my intepretation of his article. He
considred three cases.
1) Y/Z is regressed against W/Z
This is the classic Neyman example of storks bringing babies: Neyman,
J. (1952). Lectures and conferences on mathematical statistics and
probability (2nd ed.; pp. 143-154). Washington, DC: U.S. Department
of Agriculture. In applications, the same applies when per-capita
measures appear on both sides of a model equation.
2) Y is regressed against W/Z
3) Y/Z is regressed against W.
A common example of a single ratio in cases 2 & 3 is the analysis of
Body Mass Index (BMI) = Weight/Height^2 (units of Kg/M^2).
Kronmal observes that "W/Z" on the RHS of the model equation in cases
1 & 3 is an interaction term: W x (1/Z). He cites the general
principle that one should not include an interaction term without
including the main effects (W & Z, or W and 1/Z).
More basically, use of a ratio with Z in the denominator is an
attempt to "control" for Z. However control via a ratio will always
be incomplete. The question being asked in all three cases is, "If Z
is held constant, what is the relation between Y & W?" When the
question is put this way, one would control for Z by stratification
or by putting some form of Z on the RHS of the model. Another
approach would take logs of Y,W, & Z.
-Steve
On Aug 12, 2008, at 8:50 PM, Kieran McCaul wrote:
Since BMI is weight divided by height squared, why not regress
weight on
SES while adjusting for height squared?
______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2140
Phone: -61-8-9224-2140
email: [email protected]
http://myprofile.cos.com/mccaul
_______________________________________________
--- Mona Mowafi <[email protected]> wrote:
I have a dataset in which I am evaluating the effect of SES on BMI
and BMI is heavily skewed toward obesity (i.e. over 50% of the sample
30 BMI). I preferred to run a linear regression so as to use the
full range of data, but the outcome distribution violates normality
assumption and I've tried ln, log10, and sqrt transformations, none
of which work.
Is it appropriate to use tobit for modeling BMI in this instance? If
not, any suggestions?
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