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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interpretation of Two-sample t test with equal variances? |

Date |
Wed, 20 Mar 2013 16:28:29 +0000 |

Student's t test is famous for working pretty well even if the underlying assumptions are not well satisfied. One really good discussion is Miller, Rupert G. 1986, reissued 1997. Beyond ANOVA: Basics of applied statistics. New York: John Wiley; reissued London: Chapman and Hall. [now under CRC Press imprint] NB: Always capital S for Student, the pseudonym of William S. Gosset. See vignette in [R] ttest or indeed the StataCorp bookmark http://www.stata.com/giftshop/bookmarks/series1/ On Wed, Mar 20, 2013 at 4:16 PM, Gwinyai Masukume <parturitions@gmail.com> wrote: > Thank you so much everyone. Appreciated. > > David - it was indeed a very helpful discussion. > Nick - indeed those are means of maternal age. you are significant. > yes, the mother's ages are skewed. what do you mean by student's t > test works well even if you lie to it? > Carlo - it seems all the relevant independent variables have not been > included, the very low pseudo r2 is bizarre to me. > > Thanks again. > Gwinyai > > On 3/20/13, Carlo Lazzaro <carlo.lazzaro@tiscalinet.it> wrote: >> Gwinyai, >> your Pseudo R2 = 0.0015 seems very low. >> Are you sure that all the relevant independent variables have been included >> in your model? >> >> You may also consider searching for interactions between mode_delivery & >> age. >> >> Best regards, >> Carlo >> >> -----Messaggio originale----- >> Da: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Gwinyai Masukume >> Inviato: mercoledì 20 marzo 2013 06:05 >> A: statalist@hsphsun2.harvard.edu >> Oggetto: Re: st: Interpretation of Two-sample t test with equal variances? >> >> Thank you Richard. Yes, I guess the t-test suggests the counter intuitive >> though it probably won’t change things much. >> How can I reverse the situation? >> >> I ran a logistic regression for binary outcomes as you suggested: >> Essentially no significance is shown? >> >> . logit mode_delivery age >> >> Iteration 0: log likelihood = -159.58665 >> Iteration 1: log likelihood = -159.34203 >> Iteration 2: log likelihood = -159.34197 >> Iteration 3: log likelihood = -159.34197 >> >> Logistic regression Number of obs = >> 250 >> LR chi2(1) = >> 0.49 >> Prob > chi2 = >> 0.4842 >> Log likelihood = -159.34197 Pseudo R2 = >> 0.0015 >> >> ---------------------------------------------------------------------------- >> --- >> mode_delivery | Coef. Std. Err. z P>|z| [95% Conf. >> Interval] >> --------------+--------------------------------------------------------- >> --------------+------- >> age | .0155454 .0222368 0.70 0.485 -.028038 >> .0591288 >> _cons | -1.133737 .6630978 -1.71 0.087 -2.433385 >> .1659111 >> ---------------------------------------------------------------------------- >> --- >> >> With thanks, >> Gwinyai >> >> On 3/20/13, Richard Williams <richardwilliams.ndu@gmail.com> wrote: >>> Your t-test seems to suggest that age is affected by mode of delivery, >>> rather than mode of delivery is affected by age. It probably won't >>> change things much but this makes more sense to me given your >>> hypotheses: >>> >>> logit mode_delivery age >>> >>> At 11:08 PM 3/19/2013, Gwinyai Masukume wrote: >>>>Dear Stata list, >>>> >>>>I would like to double check the interpretation and appropriateness of >>>>the following statistical test I performed. >>>>My alternate hypothesis is that, “There is a difference in the baby’s >>>>mode of delivery depending on maternal age” And the null hypothesis is >>>>that, “There is no difference in the baby’s mode of delivery depending >>>>on maternal age” >>>>Looking at the output “Ha: diff != 0, Pr(|T| > |t|) = 0.4861”, I >>>>fail to reject the null hypothesis and conclude that, “There is no >>>>difference in the baby’s mode of delivery depending on maternal age” >>>> >>>>Is this a sound and appropriate interpretation? >>>> >>>>. *** Doing a T-test >>>>. ttest age, by(mode_delivery) >>>> >>>>Two-sample t test with equal variances >>>>-------------------------------------------------------------------------- >> ---- >>>> Group | Obs Mean Std. >>>> Err. Std. Dev. [95% Conf. Interval] >>>>---------+------------------------------------------------------------ >>>>---------+-------- >>>> Vaginal >>>> | 166 28.83072 .4696729 6.051313 27.90338 29.75807 >>>>C/sectio | 84 29.39524 .6579862 6.030543 28.08653 >>>> 30.70395 >>>>---------+------------------------------------------------------------ >>>>---------+-------- >>>>combined | 250 29.0204 .3818851 6.038134 28.26826 >>>> 29.77254 >>>>---------+------------------------------------------------------------ >>>>---------+-------- >>>> diff >>>> | -.5645152 .8093331 -2.158558 1.029528 >>>>-------------------------------------------------------------------------- >> ---- >>>> diff = mean(Vaginal) - >>>> mean(C/sectio) t = -0.6975 >>>>Ho: diff = 0 degrees of freedom = >>>> 248 >>>> >>>> Ha: diff < 0 Ha: diff != 0 Ha: diff >>>> > >>>> 0 >>>> Pr(T < t) = 0.2431 Pr(|T| > |t|) = >>>> 0.4861 Pr(T > t) = 0.7569 >>>> * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Interpretation of Two-sample t test with equal variances?***From:*Gwinyai Masukume <parturitions@gmail.com>

**Re: st: Interpretation of Two-sample t test with equal variances?***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: Interpretation of Two-sample t test with equal variances?***From:*Gwinyai Masukume <parturitions@gmail.com>

**R: st: Interpretation of Two-sample t test with equal variances?***From:*"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>

**Re: st: Interpretation of Two-sample t test with equal variances?***From:*Gwinyai Masukume <parturitions@gmail.com>

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