EXAMINE VARIABLES=var1[var2] … [varN] [BYfactor1[BYsubfactor1] [factor2[BYsubfactor2]] … [factor3[BYsubfactor3]] ] /STATISTICS={DESCRIPTIVES, EXTREME[(n)], ALL, NONE} /PLOT={BOXPLOT, NPPLOT, HISTOGRAM, SPREADLEVEL[(t)], ALL, NONE} /CINTERVALp/COMPARE={GROUPS,VARIABLES} /ID=identity_variable/{TOTAL,NOTOTAL} /PERCENTILE=[percentiles]={HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL } /MISSING={LISTWISE, PAIRWISE} [{EXCLUDE, INCLUDE}] [{NOREPORT,REPORT}]

The `EXAMINE`

command is used to perform exploratory data analysis.
In particular, it is useful for testing how closely a distribution follows a
normal distribution, and for finding outliers and extreme values.

The `VARIABLES`

subcommand is mandatory.
It specifies the dependent variables and optionally variables to use as
factors for the analysis.
Variables listed before the first `BY`

keyword (if any) are the
dependent variables.
The dependent variables may optionally be followed by a list of
factors which tell PSPP how to break down the analysis for each
dependent variable.

Following the dependent variables, factors may be specified.
The factors (if desired) should be preceded by a single `BY`

keyword.
The format for each factor is

factorvar[BYsubfactorvar].

Each unique combination of the values of `factorvar` and
`subfactorvar` divide the dataset into *cells*.
Statistics are calculated for each cell
and for the entire dataset (unless `NOTOTAL`

is given).

The `STATISTICS`

subcommand specifies which statistics to show.
`DESCRIPTIVES`

produces a table showing some parametric and
non-parametrics statistics.
`EXTREME`

produces a table showing the extremities of each cell.
A number in parentheses, `n` determines
how many upper and lower extremities to show.
The default number is 5.

The subcommands `TOTAL`

and `NOTOTAL`

are mutually exclusive.
If `TOTAL`

appears, then statistics for the entire dataset
as well as for each cell are produced.
If `NOTOTAL`

appears, then statistics are produced only for the cells
(unless no factor variables have been given).
These subcommands have no effect if there have been no factor variables
specified.

The `PLOT`

subcommand specifies which plots are to be produced if any.
Available plots are `HISTOGRAM`

, `NPPLOT`

, `BOXPLOT`

and
`SPREADLEVEL`

.
The first three can be used to visualise how closely each cell conforms to a
normal distribution, whilst the spread vs. level plot can be useful to visualise
how the variance differs between factors.
Boxplots show you the outliers and extreme values.
^{7}

The `SPREADLEVEL`

plot displays the interquartile range versus the
median. It takes an optional parameter `t`, which specifies how the data
should be transformed prior to plotting.
The given value `t` is a power to which the data are raised. For example, if
`t` is given as 2, then the square of the data is used.
Zero, however is a special value. If `t` is 0 or
is omitted, then data are transformed by taking its natural logarithm instead of
raising to the power of `t`.

When one or more plots are requested, `EXAMINE`

also performs the
Shapiro-Wilk test for each category.
There are however a number of provisos:

- All weight values must be integer.
- The cumulative weight value must be in the range [3, 5000]

The `COMPARE`

subcommand is only relevant if producing boxplots, and it is only
useful there is more than one dependent variable and at least one factor.
If
`/COMPARE=GROUPS`

is specified, then one plot per dependent variable is produced,
each of which contain boxplots for all the cells.
If `/COMPARE=VARIABLES`

is specified, then one plot per cell is produced,
each containing one boxplot per dependent variable.
If the `/COMPARE`

subcommand is omitted, then PSPP behaves as if
`/COMPARE=GROUPS`

were given.

The `ID`

subcommand is relevant only if `/PLOT=BOXPLOT`

or
`/STATISTICS=EXTREME`

has been given.
If given, it should provide the name of a variable which is to be used
to labels extreme values and outliers.
Numeric or string variables are permissible.
If the `ID`

subcommand is not given, then the case number is used for
labelling.

The `CINTERVAL`

subcommand specifies the confidence interval to use in
calculation of the descriptives command. The default is 95%.

The `PERCENTILES`

subcommand specifies which percentiles are to be calculated,
and which algorithm to use for calculating them. The default is to
calculate the 5, 10, 25, 50, 75, 90, 95 percentiles using the
`HAVERAGE`

algorithm.

The `TOTAL`

and `NOTOTAL`

subcommands are mutually exclusive. If `NOTOTAL`

is given and factors have been specified in the `VARIABLES`

subcommand,
then statistics for the unfactored dependent variables are
produced in addition to the factored variables. If there are no
factors specified then `TOTAL`

and `NOTOTAL`

have no effect.

The following example generates descriptive statistics and histograms for
two variables `score1` and `score2`.
Two factors are given, *viz*: `gender` and `gender` BY `culture`.
Therefore, the descriptives and histograms are generated for each
distinct value
of `gender` *and* for each distinct combination of the values
of `gender` and `race`.
Since the `NOTOTAL`

keyword is given, statistics and histograms for
`score1` and `score2` covering the whole dataset are not produced.

EXAMINEscore1score2BYgendergenderBYculture/STATISTICS = DESCRIPTIVES /PLOT = HISTOGRAM /NOTOTAL.

Here is a second example showing how the `examine`

command can be used to find extremities.

EXAMINEheightweightBYgender/STATISTICS = EXTREME (3) /PLOT = BOXPLOT /COMPARE = GROUPS /ID =name.

In this example, we look at the height and weight of a sample of individuals and
how they differ between male and female.
A table showing the 3 largest and the 3 smallest values of **height** and
**weight** for each gender, and for the whole dataset as are shown.
In addition, the `/PLOT`

subcommand requests boxplots.
Because `/COMPARE = GROUPS`

was specified, boxplots for male and female are
shown in juxtaposed in the same graphic, allowing us to easily see the difference between
the genders.
Since the variable `name` was specified on the `ID`

subcommand,
values of the `name` variable are used to label the extreme values.

**Warning!**
If you specify many dependent variables or factor variables
for which there are many distinct values, then `EXAMINE`

will produce a very
large quantity of output.

`HISTOGRAM`

uses Sturges’ rule to determine the number of
bins, as approximately *1 + \log2(n)*, where *n* is the number of samples.
Note that `FREQUENCIES`

uses a different algorithm to find the bin size.