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From |
"FEIVESON, ALAN H. (AL) (JSC-SK) (NASA)" <alan.h.feiveson@nasa.gov> |

To |
"'statalist@hsphsun2.harvard.edu'" <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 least squares won't help you. 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? Thank you for your assistance in advance. _________________________________________________________________ Help STOP SPAM with the new MSN 8 and get 2 months FREE* http://join.msn.com/?page=features/junkmail * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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