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From |
"Joseph Coveney" <[email protected]> |

To |
<[email protected]> |

Subject |
st: Re: How to compute sample size assuming a specificy accuracy in parameter estimation |

Date |
Fri, 2 Mar 2012 17:03:58 +0900 |

Tiago V. Pereira wrote: I would like to know if there is a Stata package out there that computes the sample size required to give a specific accuracy in the estimation of the population parameter (assumed to be normally distributed). For example, I would like to estimate the mean of a population having a precision of 2 in my 95% confidence intervals. Precision here is the plus and minus value used to create the confidence interval. I have guesses on the population mean, standard deviation and know the total population size. I have checked -aipe-, -sampsi-, and others, but failed to find out a specific package for that purpose. Let me know if I am missing something when using them. -------------------------------------------------------------------------------- If I understand you correctly, then you're looking for a sample size that gives a 95% confidence half-interval of 2, assuming that the population standard deviation equals some value. In lieu of an analytical solution, couldn't you just use simulation with something like the do-file below? It uses an assumed standard deviation of 10 for illustration. I suppose you could do the simulation more formally, creating a population of the known (finite) size and with the assumed standard deviation, storing it in a file, and then sampling from it with various sample sizes. I doubt that that effort is worth it, though. Joseph Coveney version 11.2 clear * set more off set seed `=date("2012-03-02", "YMD")' program define simem, rclass version 11.2 syntax , n(integer) sd(real) halfwidth(real) /// [level(real `c(level)')] quietly drop _all quietly set obs `n' tempvar tmpvar0 generate double `tmpvar0' = rnormal(0, `sd') quietly ci `tmpvar0', level(`level') return scalar success = (r(ub) - r(mean)) <= `halfwidth' end forvalues n = 100(5)130 { quietly simulate success = r(success), reps(1000) nodots: /// simem , n(`n') sd(10) halfwidth(2) summarize success, meanonly display in smcl as text "N = `n' Power = " %05.3f r(mean) if 0.95 < r(mean) continue, break } exit * * 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**:**st: How to compute sample size assuming a specificy accuracy in parameter estimation***From:*"Tiago V. Pereira" <[email protected]>

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