On Sun, Oct 14, 2018 at 10:07:09AM +0200, Dr. Oliver Walter wrote:
Am 14.10.2018 um 09:41 schrieb John Darrington:
> Which is why I suggested using one of the CDF functions.
> There is no T function, but there is a F function, which I think is the
> same if you set DF2 to
Am 14.10.2018 um 09:41 schrieb John Darrington:
Which is why I suggested using one of the CDF functions.
There is no T function, but there is a F function, which I think is the
same if you set DF2 to 1. But you probably know better than me about
those details. Perhaps IDF.F (0.05, N -1, 1) is
On Sun, Oct 14, 2018 at 09:28:47AM +0200, Dr. Oliver Walter wrote:
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
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 9
> If I understand the use case properly, I think that you can do what
> you want with with an aggregate followed by a few simple compute
> commands: [...]
Thanks!
Werner
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On Sat, Oct 13, 2018 at 04:04:41PM +0200, Werner LEMBERG wrote:
> SORT CASES BY var1 [var2].
> SPLIT FILE LAYERED BY?? var1 [var2].
>
> T-TEST /TESTVAL=0
> ?? /VARIABLES= dependent variables /MISSING=ANALYSIS
> ?? /CRITERIA=CI(insert your confidence
The results of any analysis are printed in the PSPP output and are
generally not saved in the dataset.
Am 13.10.2018 um 16:04 schrieb Werner LEMBERG:
SORT CASES BY var1 [var2].
SPLIT FILE LAYERED BY var1 [var2].
T-TEST /TESTVAL=0
/VARIABLES= dependent variables /MISSING=ANALYSIS
> SORT CASES BY var1 [var2].
> SPLIT FILE LAYERED BY var1 [var2].
>
> T-TEST /TESTVAL=0
> /VARIABLES= dependent variables /MISSING=ANALYSIS
> /CRITERIA=CI(insert your confidence level here, e.g. 0.95).
Very nice, thanks!
> Then you can use the means and the bounds of the confidence
I don't think that PSPP can produce bar charts with confidence intervals
or something similar (bar charts for means aren't the best idea
anyway). I think it is only possible to split the data file to compare
groups and then calculate confidence intervals for the mean for these
groups.
Comman
> It seems to be a mixed ANCOVA with a within-subjects factor called
> "Location", a between-subjects factor called "Group" and a covariate
> "Age". I think that the GLM command in PSPP is not able to compute
> such an analysis. GLM can only compute between-subjects designs in
> PSPP (cf. PSPP m
It seems to be a mixed ANCOVA with a within-subjects factor called
"Location", a between-subjects factor called "Group" and a covariate
"Age". I think that the GLM command in PSPP is not able to compute such
an analysis. GLM can only compute between-subjects designs in PSPP (cf.
PSPP manual, p.
> I just responded to your statements about the relations between CIs
> and hypothesis test that a CI is *not* always associated with a
> hypothesis. The equations I mentioned were only examples for a
> confidence interval and its equivalent hypothesis test. [...]
Thanks a lot to all who have re
I just responded to your statements about the relations between CIs and
hypothesis test that a CI is *not* always associated with a hypothesis.
The equations I mentioned were only examples for a confidence interval
and its equivalent hypothesis test.
BTW: It's not safe to always use z instead
This is a good point, yes. I'm not the original requester, but I think they
were really asking for a simple way to get a CI when reporting
summary/descriptive statistics (without having a second mean to compare
to). In SPSS you can do this:
https://en.wikibooks.org/wiki/Using_SPSS_and_PASW/Confiden
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