5.7.10 Statistical Distribution Functions
PSPP can calculate several functions of standard statistical
distributions. These functions are named systematically based on the
function and the distribution. The table below describes the
statistical distribution functions in general:
- PDF.dist (x[, param...])
- Probability density function for dist. The domain of x
depends on dist. For continuous distributions, the result is
the density of the probability function at x, and the range is
nonnegative real numbers. For discrete distributions, the result is
the probability of x.
- CDF.dist (x[, param...])
- Cumulative distribution function for dist, that is, the
probability that a random variate drawn from the distribution is less
than x. The domain of x depends dist. The result is
a probability.
- SIG.dist (x[, param...)
- Tail probability function for dist, that is, the probability
that a random variate drawn from the distribution is greater than
x. The domain of x depends dist. The result is a
probability. Only a few distributions include an SIG function.
- IDF.dist (p[, param...])
- Inverse distribution function for dist, the value of x for
which the CDF would yield p. The value of p is a
probability. The range depends on dist and is identical to the
domain for the corresponding CDF.
- RV.dist ([param...])
- Random variate function for dist. The range depends on the
distribution.
- NPDF.dist (x[, param...])
- Noncentral probability density function. The result is the density of
the given noncentral distribution at x. The domain of x
depends on dist. The range is nonnegative real numbers. Only a
few distributions include an NPDF function.
- NCDF.dist (x[, param...])
- Noncentral cumulative distribution function for dist, that is,
the probability that a random variate drawn from the given noncentral
distribution is less than x. The domain of x depends
dist. The result is a probability. Only a few distributions
include an NCDF function.
The individual distributions are described individually below.