A confidence interval is mathematically equivalent to its
corresponding hypothesis test. The hypothesis test is significant
if the corresponding confidence interval does not contain the
parameter value of the null hypothesis. The confidence interval
does not contain the parameter value of the null hypothesis if the
hypothesis test is significant. Hence, wether you calculate the
confidence interval or conduct the hypothesis test, doesn't really
matter.
mean(X) +/- t * sd/sqrt(n): confidence interval for the expected
value of X, mu, X normally distributed with unknown population
variance
t = (mean - mü0)/ (sd/sqrt(n)) : test statistic for testing if mu
equals the value in the null hypothesis, mu0, X normally
distributed with unknown population variance
If mü0 is not contained in the confidence interval, the
hypothesis test is significant.
Dr. Oliver Walter
Am 12.10.2018 um 15:01 schrieb Mark
Hancock:
I unfortunately don't know enough about PSPP
syntax to suggest how to do this, but a CI is
not always
associated with a hypothesis and can be calculated from just a
mean and SD (and a cumulative distribution function, which is
typically the normal one). Typically the formula is something
like:
mean ± z(SD/sqrt(n)), where z is from the CDF.
On Fri, Oct 12, 2018 at 6:29 AM John Darrington
<
address@hidden>
wrote:
The confidence interval is a
concept associated with a hypothesis.
If it's the confidence interval on the test for a mean value,
typically you
would get that by using a T-Test.
On Fri, Oct 12, 2018 at 10:40:22AM +0200, Werner LEMBERG
wrote:
Folks,
I would like to get a 95% confidence interval so that I
could use it
in AGGREGATE, e.g.,
AGGREGATE OUTFILE * MODE ADDVARIABLES
/BREAK=...
/Mean = mean(V)
/CI = ci(V, 0.95)
What must I do to get the result of my hypothetical `ci'
function?
I'm a PSPP novice, so maybe there is a better solution
than AGGREGATE
??? what I ultimately want is to emit the confidence
interval of a
variable to a CSV file using SAVE TRANSLATE.
Werner
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