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Re: st: Using natural logs on RHS of maximum likelihood models

 From David Hoaglin To statalist@hsphsun2.harvard.edu Subject Re: st: Using natural logs on RHS of maximum likelihood models Date Tue, 26 Mar 2013 22:18:50 -0400

```Dear Sam,

Before anyone can make a constructive suggestion, you need to share
a method of estimating the parameters in a model.  It applies to a
very wide range of models, some of which have a dichotomous (0, 1)
outcome variable.  Is your model a logistic regression (logit) model,
a probit model, or another type entirely?  Please be specific.

simple answer.  The aim is usually to express each predictor variable
in a form that appropriately captures its relation to the outcome
variable (after adjusting for the contributions of the other predictor
variables).  Generically, we could write something like

g(y) = b0 + b1*f1(x1) + b2*f2(x2) + (more predictors)

The functions g, f1, f2, etc. may differ as needed, and common choices
include "leave it alone," take the logarithm, take the square root,
and "square it."  Part of your challenge in analyzing data is to make
appropriate choices of such functions.  For some classes of models,
people have developed a variety of diagnostic tools that help this
context of your data, I or someone else reading this list may be able
to recommend a book that discusses this and other steps in the
model-building process and shows how they work on actual sets of data.

Regards,

David Hoaglin

On Tue, Mar 26, 2013 at 5:19 PM, SAM MCCAW <sam2stata@gmail.com> wrote:
> Hello All,
>
> I am running a maximum likelihood model with a (0,1) categorical
> dependent variable.
>
> On the right hand side is better to use natural logs of non
> categorical variables or leave them as is as real numbers?
>
> Thanks a bunch.
>
> SAM
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