# st: RE: arcsine(square root)

 From "FEIVESON, ALAN H. (AL) (JSC-SK) (NASA)" To "'statalist@hsphsun2.harvard.edu'" Subject st: RE: arcsine(square root) Date Wed, 31 Dec 2003 08:40:05 -0600

```Yoshiro - In answer to your questions:

1. Is your "y" a binomial proportion? The arcsine(sqrt(y)) transformation
was designed to make biniomial proportions closer to being normally
distributed with equal variance. With glm and a binomial family you can
indeed do regression analysis on you original variable.

2. Nonlinear least squares applies to the way parameters occur in the
regression model. The non-linearity has nothing to do with the distribution
of the dependent variable. (Any kind of)least squares is equivalent to
maximumum likelihood when you have a homoscedastic normal dependent
variable. So if you don't have a normally distributed variable, noinlinear

3. There is no built-in Stata command for doing stepwise glm, etc.This
question has been asked before - there may be an FAQ on it.

Al Feiveson

I am applying regression analysis
to a dependent variable 'y', which has non-normal
distribution.

arcsine(square root( y )) considerably
transformed 'y' to a normal distribution.

(1) However, are there any better method (such as glm ?)
to apply regression analysis to this y as it is?

(2) Are there advantages in using non-linear least square method (nl)
for this case?

(3) In addition, if  "regress" command is to be used,
are there any stepwise command, equivalent to "sw"
for maximum likelihood estimate?

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