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From |
Jeph Herrin <jeph.herrin@yale.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: AW: ratio function |

Date |
Fri, 02 Apr 2010 07:53:06 -0400 |

if you know the mean (E) and variance (V) of X and Y, you can calculate the first order approximation: E(X/Y) = E(X)/E(Y) V(X/Y) = E(X/Y)^2 * (V(X)/E(X)^2 + V(Y)/V(Y)^2 + COV(X,Y)/E(X)E(Y)) at least, that's what they taught us in physics lab. hth, Jeph Roman Kasal wrote:

I don't agree...so how to do it when you want to find out ratio between years, male X female, ...? So there is no solution? Just to keep N,mean, SE, degrees of freedom, N_strata, N_psu, .... and calculate it manually? I think it is not appropriate solution, at least to have it as an option. I think there is missing a lot with complex survey in Stata and complex survey is needed for almost every survey research, even freeware R-project is better equipped :( so have a hope Stata will get it soon....immediately we are buying it again :)And it should. Data (x,y) (1,2) (2,4) (3,6) (100,.) will give an entirely different view of the data if the unpaired observation is included in a mean or ratio calculation. Or consider data with x missing in half the pairs and y missing in the other half; the ratio of means would be meaningless. The formulas for standard errors for ratios assume that the data are paired. Formally, they are based on the residual MSE of a regression of y on x through the origin. You cannot do that regression with unpaired data. If your concern is missing data, the solution is to impute the missing values before analysis. Steve * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: Re: st: RE: AW: ratio function***From:*"Roman Kasal" <kasal@trexima.cz>

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