Am 14.10.2018 um 08:46 schrieb John Darrington:
AGGREGATE OUTFILE * MODE ADDVARIABLES /BREAK=g /Mean = mean(V) /sd = sd(v) /n = n(v) .compute ci_upper=mean + sd/sqrt(n). compute ci_lower=mean - sd/sqrt(n). list.
Sorry for interrupting, but this doesn't give a 95% (or 90%) CI, but only mean +/- one standard error which is a 68%-CI if X is normally distributed and sd equals the population variance or an approximate 68% CI if the sample size goes to infinity (is large). You have to include a t value into the equation for calculating a 95% (or 90%) CI. If your sample sizes are small and differ from each other you should use different t values for each CI and each group. If you sample size is large you could use one z value (1.96) for all groups, but this is not appropriate in this case (n1 = n2 = 15, sample sizes are too small for this standard normal approximation).
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