+@node FACTOR
+@section FACTOR
+
+@vindex FACTOR
+@cindex factor analysis
+@cindex principal components analysis
+@cindex principal axis factoring
+@cindex data reduction
+
+@display
+FACTOR VARIABLES=@var{var_list}
+
+ [ /METHOD = @{CORRELATION, COVARIANCE@} ]
+
+ [ /EXTRACTION=@{PC, PAF@}]
+
+ [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, NOROTATE@}]
+
+ [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [SIG] [ALL] [DEFAULT] ]
+
+ [ /PLOT=[EIGEN] ]
+
+ [ /FORMAT=[SORT] [BLANK(@var{n})] [DEFAULT] ]
+
+ [ /CRITERIA=[FACTORS(@var{n})] [MINEIGEN(@var{l})] [ITERATE(@var{m})] [ECONVERGE (@var{delta})] [DEFAULT] ]
+
+ [ /MISSING=[@{LISTWISE, PAIRWISE@}] [@{INCLUDE, EXCLUDE@}] ]
+@end display
+
+The @cmd{FACTOR} command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find
+common factors in the data or for data reduction purposes.
+
+The @subcmd{VARIABLES} subcommand is required. It lists the variables which are to partake in the analysis.
+
+The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors (components) are extracted from the data.
+If @subcmd{PC} is specified, then Principal Components Analysis is used.
+If @subcmd{PAF} is specified, then Principal Axis Factoring is
+used. By default Principal Components Analysis will be used.
+
+The @subcmd{/ROTATION} subcommand is used to specify the method by which the extracted solution will be rotated.
+Three methods are available: @subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
+If don't want any rotation to be performed, the word @subcmd{NOROTATE} will prevent the command from performing any
+rotation on the data. Oblique rotations are not supported.
+
+The @subcmd{/METHOD} subcommand should be used to determine whether the covariance matrix or the correlation matrix of the data is
+to be analysed. By default, the correlation matrix is analysed.
+
+The @subcmd{/PRINT} subcommand may be used to select which features of the analysis are reported:
+
+@itemize
+@item @subcmd{UNIVARIATE}
+ A table of mean values, standard deviations and total weights are printed.
+@item @subcmd{INITIAL}
+ Initial communalities and eigenvalues are printed.
+@item @subcmd{EXTRACTION}
+ Extracted communalities and eigenvalues are printed.
+@item @subcmd{ROTATION}
+ Rotated communalities and eigenvalues are printed.
+@item @subcmd{CORRELATION}
+ The correlation matrix is printed.
+@item @subcmd{COVARIANCE}
+ The covariance matrix is printed.
+@item @subcmd{DET}
+ The determinant of the correlation or covariance matrix is printed.
+@item @subcmd{KMO}
+ The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
+@item @subcmd{SIG}
+ The significance of the elements of correlation matrix is printed.
+@item @subcmd{ALL}
+ All of the above are printed.
+@item @subcmd{DEFAULT}
+ Identical to @subcmd{INITIAL} and @subcmd{EXTRACTION}.
+@end itemize
+
+If @subcmd{/PLOT=EIGEN} is given, then a ``Scree'' plot of the eigenvalues will be printed. This can be useful for visualizing
+which factors (components) should be retained.
+
+The @subcmd{/FORMAT} subcommand determined how data are to be displayed in loading matrices. If @subcmd{SORT} is specified, then the variables
+are sorted in descending order of significance. If @subcmd{BLANK(@var{n})} is specified, then coefficients whose absolute value is less
+than @var{n} will not be printed. If the keyword @subcmd{DEFAULT} is given, or if no @subcmd{/FORMAT} subcommand is given, then no sorting is
+performed, and all coefficients will be printed.
+
+The @subcmd{/CRITERIA} subcommand is used to specify how the number of extracted factors (components) are chosen.
+If @subcmd{FACTORS(@var{n})} is
+specified, where @var{n} is an integer, then @var{n} factors will be extracted. Otherwise, the @subcmd{MINEIGEN} setting will
+be used.
+@subcmd{MINEIGEN(@var{l})} requests that all factors whose eigenvalues are greater than or equal to @var{l} are extracted.
+The default value of @var{l} is 1.
+The @subcmd{ECONVERGE} setting has effect only when iterative algorithms for factor
+extraction (such as Principal Axis Factoring) are used.
+@subcmd{ECONVERGE(@var{delta})} specifies that
+iteration should cease when
+the maximum absolute value of the communality estimate between one iteration and the previous is less than @var{delta}. The
+default value of @var{delta} is 0.001.
+The @subcmd{ITERATE(@var{m})} may appear any number of times and is used for two different purposes.
+It is used to set the maximum number of iterations (@var{m}) for convergence and also to set the maximum number of iterations
+for rotation.
+Whether it affects convergence or rotation depends upon which subcommand follows the @subcmd{ITERATE} subcommand.
+If @subcmd{EXTRACTION} follows, it affects convergence.
+If @subcmd{ROTATION} follows, it affects rotation.
+If neither @subcmd{ROTATION} nor @subcmd{EXTRACTION} follow a @subcmd{ITERATE} subcommand it will be ignored.
+The default value of @var{m} is 25.
+
+The @cmd{MISSING} subcommand determines the handling of missing variables.
+If @subcmd{INCLUDE} is set, then user-missing values are included in the
+calculations, but system-missing values are not.
+If @subcmd{EXCLUDE} is set, which is the default, user-missing
+values are excluded as well as system-missing values.
+This is the default.
+If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
+whenever any variable specified in the @cmd{VARIABLES} subcommand
+contains a missing value.
+If @subcmd{PAIRWISE} is set, then a case is considered missing only if either of the
+values for the particular coefficient are missing.
+The default is @subcmd{LISTWISE}.
+
+@node LOGISTIC REGRESSION
+@section LOGISTIC REGRESSION
+
+@vindex LOGISTIC REGRESSION
+@cindex logistic regression
+@cindex bivariate logistic regression
+
+@display
+LOGISTIC REGRESSION [VARIABLES =] @var{dependent_var} WITH @var{predictors}
+
+ [/CATEGORICAL = @var{categorical_predictors}]
+
+ [@{/NOCONST | /ORIGIN | /NOORIGIN @}]
+
+ [/PRINT = [SUMMARY] [DEFAULT] [CI(@var{confidence})] [ALL]]
+
+ [/CRITERIA = [BCON(@var{min_delta})] [ITERATE(@var{max_interations})]
+ [LCON(@var{min_likelihood_delta})] [EPS(@var{min_epsilon})]
+ [CUT(@var{cut_point})]]
+
+ [/MISSING = @{INCLUDE|EXCLUDE@}]
+@end display
+
+Bivariate Logistic Regression is used when you want to explain a dichotomous dependent
+variable in terms of one or more predictor variables.
+
+The minimum command is
+@example
+LOGISTIC REGRESSION @var{y} WITH @var{x1} @var{x2} @dots{} @var{xn}.
+@end example
+Here, @var{y} is the dependent variable, which must be dichotomous and @var{x1} @dots{} @var{xn}
+are the predictor variables whose coefficients the procedure estimates.
+
+By default, a constant term is included in the model.
+Hence, the full model is
+@math{
+{\bf y}
+= b_0 + b_1 {\bf x_1}
++ b_2 {\bf x_2}
++ \dots
++ b_n {\bf x_n}
+}
+
+Predictor variables which are categorical in nature should be listed on the @subcmd{/CATEGORICAL} subcommand.
+Simple variables as well as interactions between variables may be listed here.
+
+If you want a model without the constant term @math{b_0}, use the keyword @subcmd{/ORIGIN}.
+@subcmd{/NOCONST} is a synonym for @subcmd{/ORIGIN}.
+
+An iterative Newton-Raphson procedure is used to fit the model.
+The @subcmd{/CRITERIA} subcommand is used to specify the stopping criteria of the procedure,
+and other parameters.
+The value of @var{cut_point} is used in the classification table. It is the
+threshold above which predicted values are considered to be 1. Values
+of @var{cut_point} must lie in the range [0,1].
+During iterations, if any one of the stopping criteria are satisfied, the procedure is
+considered complete.
+The stopping criteria are:
+@itemize
+@item The number of iterations exceeds @var{max_iterations}.
+ The default value of @var{max_iterations} is 20.
+@item The change in the all coefficient estimates are less than @var{min_delta}.
+The default value of @var{min_delta} is 0.001.
+@item The magnitude of change in the likelihood estimate is less than @var{min_likelihood_delta}.
+The default value of @var{min_delta} is zero.
+This means that this criterion is disabled.
+@item The differential of the estimated probability for all cases is less than @var{min_epsilon}.
+In other words, the probabilities are close to zero or one.
+The default value of @var{min_epsilon} is 0.00000001.
+@end itemize
+
+
+The @subcmd{PRINT} subcommand controls the display of optional statistics.
+Currently there is one such option, @subcmd{CI}, which indicates that the
+confidence interval of the odds ratio should be displayed as well as its value.
+@subcmd{CI} should be followed by an integer in parentheses, to indicate the
+confidence level of the desired confidence interval.
+
+The @subcmd{MISSING} subcommand determines the handling of missing
+variables.
+If @subcmd{INCLUDE} is set, then user-missing values are included in the
+calculations, but system-missing values are not.
+If @subcmd{EXCLUDE} is set, which is the default, user-missing
+values are excluded as well as system-missing values.
+This is the default.
+
+@node MEANS
+@section MEANS
+
+@vindex MEANS
+@cindex means
+
+@display
+MEANS [TABLES =]
+ @{@var{var_list}@}
+ [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]]
+
+ [ /@{@var{var_list}@}
+ [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]] ]
+
+ [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
+ [VARIANCE] [KURT] [SEKURT]
+ [SKEW] [SESKEW] [FIRST] [LAST]
+ [HARMONIC] [GEOMETRIC]
+ [DEFAULT]
+ [ALL]
+ [NONE] ]
+
+ [/MISSING = [TABLE] [INCLUDE] [DEPENDENT]]
+@end display
+
+You can use the @cmd{MEANS} command to calculate the arithmetic mean and similar
+statistics, either for the dataset as a whole or for categories of data.
+
+The simplest form of the command is
+@example
+MEANS @var{v}.
+@end example
+@noindent which calculates the mean, count and standard deviation for @var{v}.
+If you specify a grouping variable, for example
+@example
+MEANS @var{v} BY @var{g}.
+@end example
+@noindent then the means, counts and standard deviations for @var{v} after having
+been grouped by @var{g} will be calculated.
+Instead of the mean, count and standard deviation, you could specify the statistics
+in which you are interested:
+@example
+MEANS @var{x} @var{y} BY @var{g}
+ /CELLS = HARMONIC SUM MIN.
+@end example
+This example calculates the harmonic mean, the sum and the minimum values of @var{x} and @var{y}
+grouped by @var{g}.
+
+The @subcmd{CELLS} subcommand specifies which statistics to calculate. The available statistics
+are:
+@itemize
+@item @subcmd{MEAN}
+@cindex arithmetic mean
+ The arithmetic mean.
+@item @subcmd{COUNT}
+ The count of the values.
+@item @subcmd{STDDEV}
+ The standard deviation.
+@item @subcmd{SEMEAN}
+ The standard error of the mean.
+@item @subcmd{SUM}
+ The sum of the values.
+@item @subcmd{MIN}
+ The minimum value.
+@item @subcmd{MAX}
+ The maximum value.
+@item @subcmd{RANGE}
+ The difference between the maximum and minimum values.
+@item @subcmd{VARIANCE}
+ The variance.
+@item @subcmd{FIRST}
+ The first value in the category.
+@item @subcmd{LAST}
+ The last value in the category.
+@item @subcmd{SKEW}
+ The skewness.
+@item @subcmd{SESKEW}
+ The standard error of the skewness.
+@item @subcmd{KURT}
+ The kurtosis
+@item @subcmd{SEKURT}
+ The standard error of the kurtosis.
+@item @subcmd{HARMONIC}
+@cindex harmonic mean
+ The harmonic mean.
+@item @subcmd{GEOMETRIC}
+@cindex geometric mean
+ The geometric mean.
+@end itemize
+
+In addition, three special keywords are recognized:
+@itemize
+@item @subcmd{DEFAULT}
+ This is the same as @subcmd{MEAN} @subcmd{COUNT} @subcmd{STDDEV}.
+@item @subcmd{ALL}
+ All of the above statistics will be calculated.
+@item @subcmd{NONE}
+ No statistics will be calculated (only a summary will be shown).
+@end itemize
+
+
+More than one @dfn{table} can be specified in a single command.
+Each table is separated by a @samp{/}. For
+example
+@example
+MEANS TABLES =
+ @var{c} @var{d} @var{e} BY @var{x}
+ /@var{a} @var{b} BY @var{x} @var{y}
+ /@var{f} BY @var{y} BY @var{z}.
+@end example
+has three tables (the @samp{TABLE =} is optional).
+The first table has three dependent variables @var{c}, @var{d} and @var{e}
+and a single categorical variable @var{x}.
+The second table has two dependent variables @var{a} and @var{b},
+and two categorical variables @var{x} and @var{y}.
+The third table has a single dependent variables @var{f}
+and a categorical variable formed by the combination of @var{y} and @var{z}.
+
+
+By default values are omitted from the analysis only if missing values
+(either system missing or user missing)
+for any of the variables directly involved in their calculation are
+encountered.
+This behaviour can be modified with the @subcmd{/MISSING} subcommand.
+Three options are possible: @subcmd{TABLE}, @subcmd{INCLUDE} and @subcmd{DEPENDENT}.
+
+@subcmd{/MISSING = TABLE} causes cases to be dropped if any variable is missing
+in the table specification currently being processed, regardless of
+whether it is needed to calculate the statistic.
+
+@subcmd{/MISSING = INCLUDE} says that user missing values, either in the dependent
+variables or in the categorical variables should be taken at their face
+value, and not excluded.
+
+@subcmd{/MISSING = DEPENDENT} says that user missing values, in the dependent
+variables should be taken at their face value, however cases which
+have user missing values for the categorical variables should be omitted
+from the calculation.
+
+@node NPAR TESTS
+@section NPAR TESTS
+
+@vindex NPAR TESTS
+@cindex nonparametric tests
+
+@display
+NPAR TESTS
+
+ nonparametric test subcommands
+ .
+ .
+ .
+
+ [ /STATISTICS=@{DESCRIPTIVES@} ]
+
+ [ /MISSING=@{ANALYSIS, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
+
+ [ /METHOD=EXACT [ TIMER [(@var{n})] ] ]
+@end display
+
+@cmd{NPAR TESTS} performs nonparametric tests.
+Non parametric tests make very few assumptions about the distribution of the
+data.
+One or more tests may be specified by using the corresponding subcommand.
+If the @subcmd{/STATISTICS} subcommand is also specified, then summary statistics are
+produces for each variable that is the subject of any test.
+
+Certain tests may take a long time to execute, if an exact figure is required.
+Therefore, by default asymptotic approximations are used unless the
+subcommand @subcmd{/METHOD=EXACT} is specified.
+Exact tests give more accurate results, but may take an unacceptably long
+time to perform. If the @subcmd{TIMER} keyword is used, it sets a maximum time,
+after which the test will be abandoned, and a warning message printed.
+The time, in minutes, should be specified in parentheses after the @subcmd{TIMER} keyword.
+If the @subcmd{TIMER} keyword is given without this figure, then a default value of 5 minutes
+is used.
+
+
+@menu
+* BINOMIAL:: Binomial Test
+* CHISQUARE:: Chisquare Test
+* COCHRAN:: Cochran Q Test
+* FRIEDMAN:: Friedman Test
+* KENDALL:: Kendall's W Test
+* KOLMOGOROV-SMIRNOV:: Kolmogorov Smirnov Test
+* KRUSKAL-WALLIS:: Kruskal-Wallis Test
+* MANN-WHITNEY:: Mann Whitney U Test
+* MCNEMAR:: McNemar Test
+* MEDIAN:: Median Test
+* RUNS:: Runs Test
+* SIGN:: The Sign Test
+* WILCOXON:: Wilcoxon Signed Ranks Test
+@end menu
+
+
+@node BINOMIAL
+@subsection Binomial test
+@vindex BINOMIAL
+@cindex binomial test
+
+@display
+ [ /BINOMIAL[(@var{p})]=@var{var_list}[(@var{value1}[, @var{value2})] ] ]
+@end display
+
+The @subcmd{/BINOMIAL} subcommand compares the observed distribution of a dichotomous
+variable with that of a binomial distribution.
+The variable @var{p} specifies the test proportion of the binomial
+distribution.
+The default value of 0.5 is assumed if @var{p} is omitted.
+
+If a single value appears after the variable list, then that value is
+used as the threshold to partition the observed values. Values less
+than or equal to the threshold value form the first category. Values
+greater than the threshold form the second category.
+
+If two values appear after the variable list, then they will be used
+as the values which a variable must take to be in the respective
+category.
+Cases for which a variable takes a value equal to neither of the specified
+values, take no part in the test for that variable.
+
+If no values appear, then the variable must assume dichotomous
+values.
+If more than two distinct, non-missing values for a variable
+under test are encountered then an error occurs.
+
+If the test proportion is equal to 0.5, then a two tailed test is
+reported. For any other test proportion, a one tailed test is
+reported.
+For one tailed tests, if the test proportion is less than
+or equal to the observed proportion, then the significance of
+observing the observed proportion or more is reported.
+If the test proportion is more than the observed proportion, then the
+significance of observing the observed proportion or less is reported.
+That is to say, the test is always performed in the observed
+direction.
+
+@pspp{} uses a very precise approximation to the gamma function to
+compute the binomial significance. Thus, exact results are reported
+even for very large sample sizes.
+
+
+
+@node CHISQUARE
+@subsection Chisquare Test
+@vindex CHISQUARE
+@cindex chisquare test
+
+
+@display
+ [ /CHISQUARE=@var{var_list}[(@var{lo},@var{hi})] [/EXPECTED=@{EQUAL|@var{f1}, @var{f2} @dots{} @var{fn}@}] ]
+@end display
+
+
+The @subcmd{/CHISQUARE} subcommand produces a chi-square statistic for the differences
+between the expected and observed frequencies of the categories of a variable.
+Optionally, a range of values may appear after the variable list.
+If a range is given, then non integer values are truncated, and values
+outside the specified range are excluded from the analysis.
+
+The @subcmd{/EXPECTED} subcommand specifies the expected values of each
+category.
+There must be exactly one non-zero expected value, for each observed
+category, or the @subcmd{EQUAL} keywork must be specified.
+You may use the notation @subcmd{@var{n}*@var{f}} to specify @var{n}
+consecutive expected categories all taking a frequency of @var{f}.
+The frequencies given are proportions, not absolute frequencies. The
+sum of the frequencies need not be 1.
+If no @subcmd{/EXPECTED} subcommand is given, then then equal frequencies
+are expected.
+
+
+@node COCHRAN
+@subsection Cochran Q Test
+@vindex Cochran
+@cindex Cochran Q test
+@cindex Q, Cochran Q
+
+@display
+ [ /COCHRAN = @var{var_list} ]
+@end display
+
+The Cochran Q test is used to test for differences between three or more groups.
+The data for @var{var_list} in all cases must assume exactly two distinct values (other than missing values).
+
+The value of Q will be displayed and its Asymptotic significance based on a chi-square distribution.
+
+@node FRIEDMAN
+@subsection Friedman Test
+@vindex FRIEDMAN
+@cindex Friedman test
+
+@display
+ [ /FRIEDMAN = @var{var_list} ]
+@end display
+
+The Friedman test is used to test for differences between repeated measures when
+there is no indication that the distributions are normally distributed.
+
+A list of variables which contain the measured data must be given. The procedure
+prints the sum of ranks for each variable, the test statistic and its significance.
+
+@node KENDALL
+@subsection Kendall's W Test
+@vindex KENDALL
+@cindex Kendall's W test
+@cindex coefficient of concordance
+
+@display
+ [ /KENDALL = @var{var_list} ]
+@end display
+
+The Kendall test investigates whether an arbitrary number of related samples come from the
+same population.
+It is identical to the Friedman test except that the additional statistic W, Kendall's Coefficient of Concordance is printed.
+It has the range [0,1] --- a value of zero indicates no agreement between the samples whereas a value of
+unity indicates complete agreement.
+
+
+@node KOLMOGOROV-SMIRNOV
+@subsection Kolmogorov-Smirnov Test
+@vindex KOLMOGOROV-SMIRNOV
+@vindex K-S
+@cindex Kolmogorov-Smirnov test
+
+@display
+ [ /KOLMOGOROV-SMIRNOV (@{NORMAL [@var{mu}, @var{sigma}], UNIFORM [@var{min}, @var{max}], POISSON [@var{lambda}], EXPONENTIAL [@var{scale}] @}) = @var{var_list} ]
+@end display
+
+The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is
+drawn from a particular distribution. Four distributions are supported, @i{viz:}
+Normal, Uniform, Poisson and Exponential.
+
+Ideally you should provide the parameters of the distribution against which you wish to test
+the data. For example, with the normal distribution the mean (@var{mu})and standard deviation (@var{sigma})
+should be given; with the uniform distribution, the minimum (@var{min})and maximum (@var{max}) value should
+be provided.
+However, if the parameters are omitted they will be imputed from the data. Imputing the
+parameters reduces the power of the test so should be avoided if possible.
+
+In the following example, two variables @var{score} and @var{age} are tested to see if
+they follow a normal distribution with a mean of 3.5 and a standard deviation of 2.0.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
+@end example
+If the variables need to be tested against different distributions, then a separate
+subcommand must be used. For example the following syntax tests @var{score} against
+a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst @var{age}
+is tested against a normal distribution of mean 40 and standard deviation 1.5.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score}
+ /KOLMOGOROV-SMIRNOV (normal 40 1.5) = @var{age}.
+@end example
+
+The abbreviated subcommand @subcmd{K-S} may be used in place of @subcmd{KOLMOGOROV-SMIRNOV}.
+
+@node KRUSKAL-WALLIS
+@subsection Kruskal-Wallis Test
+@vindex KRUSKAL-WALLIS
+@vindex K-W
+@cindex Kruskal-Wallis test
+
+@display
+ [ /KRUSKAL-WALLIS = @var{var_list} BY var (@var{lower}, @var{upper}) ]
+@end display
+
+The Kruskal-Wallis test is used to compare data from an
+arbitrary number of populations. It does not assume normality.
+The data to be compared are specified by @var{var_list}.
+The categorical variable determining the groups to which the
+data belongs is given by @var{var}. The limits @var{lower} and
+@var{upper} specify the valid range of @var{var}. Any cases for
+which @var{var} falls outside [@var{lower}, @var{upper}] will be
+ignored.
+
+The mean rank of each group as well as the chi-squared value and significance
+of the test will be printed.
+The abbreviated subcommand @subcmd{K-W} may be used in place of @subcmd{KRUSKAL-WALLIS}.
+
+
+@node MANN-WHITNEY
+@subsection Mann-Whitney U Test
+@vindex MANN-WHITNEY
+@vindex M-W
+@cindex Mann-Whitney U test
+@cindex U, Mann-Whitney U
+
+@display
+ [ /MANN-WHITNEY = @var{var_list} BY var (@var{group1}, @var{group2}) ]
+@end display
+
+The Mann-Whitney subcommand is used to test whether two groups of data come from different populations.
+The variables to be tested should be specified in @var{var_list} and the grouping variable, that determines to which group the test variables belong, in @var{var}.
+@var{Var} may be either a string or an alpha variable.
+@var{Group1} and @var{group2} specify the
+two values of @var{var} which determine the groups of the test data.
+Cases for which the @var{var} value is neither @var{group1} or @var{group2} will be ignored.
+
+The value of the Mann-Whitney U statistic, the Wilcoxon W, and the significance will be printed.
+The abbreviated subcommand @subcmd{M-W} may be used in place of @subcmd{MANN-WHITNEY}.
+
+@node MCNEMAR
+@subsection McNemar Test
+@vindex MCNEMAR
+@cindex McNemar test
+
+@display
+ [ /MCNEMAR @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
+@end display
+
+Use McNemar's test to analyse the significance of the difference between
+pairs of correlated proportions.
+
+If the @code{WITH} keyword is omitted, then tests for all
+combinations of the listed variables are performed.
+If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
+is also given, then the number of variables preceding @code{WITH}
+must be the same as the number following it.
+In this case, tests for each respective pair of variables are
+performed.
+If the @code{WITH} keyword is given, but the
+@code{(PAIRED)} keyword is omitted, then tests for each combination
+of variable preceding @code{WITH} against variable following
+@code{WITH} are performed.
+
+The data in each variable must be dichotomous. If there are more
+than two distinct variables an error will occur and the test will
+not be run.
+
+@node MEDIAN
+@subsection Median Test
+@vindex MEDIAN
+@cindex Median test
+
+@display
+ [ /MEDIAN [(@var{value})] = @var{var_list} BY @var{variable} (@var{value1}, @var{value2}) ]
+@end display
+
+The median test is used to test whether independent samples come from
+populations with a common median.
+The median of the populations against which the samples are to be tested
+may be given in parentheses immediately after the
+@subcmd{/MEDIAN} subcommand. If it is not given, the median will be imputed from the
+union of all the samples.
+
+The variables of the samples to be tested should immediately follow the @samp{=} sign. The
+keyword @code{BY} must come next, and then the grouping variable. Two values
+in parentheses should follow. If the first value is greater than the second,
+then a 2 sample test is performed using these two values to determine the groups.
+If however, the first variable is less than the second, then a @i{k} sample test is
+conducted and the group values used are all values encountered which lie in the
+range [@var{value1},@var{value2}].
+
+
+@node RUNS
+@subsection Runs Test
+@vindex RUNS
+@cindex runs test
+
+@display
+ [ /RUNS (@{MEAN, MEDIAN, MODE, @var{value}@}) = @var{var_list} ]
+@end display
+
+The @subcmd{/RUNS} subcommand tests whether a data sequence is randomly ordered.
+
+It works by examining the number of times a variable's value crosses a given threshold.
+The desired threshold must be specified within parentheses.
+It may either be specified as a number or as one of @subcmd{MEAN}, @subcmd{MEDIAN} or @subcmd{MODE}.
+Following the threshold specification comes the list of variables whose values are to be
+tested.
+
+The subcommand shows the number of runs, the asymptotic significance based on the
+length of the data.
+
+@node SIGN
+@subsection Sign Test
+@vindex SIGN
+@cindex sign test
+
+@display
+ [ /SIGN @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
+@end display
+
+The @subcmd{/SIGN} subcommand tests for differences between medians of the
+variables listed.
+The test does not make any assumptions about the
+distribution of the data.
+
+If the @code{WITH} keyword is omitted, then tests for all
+combinations of the listed variables are performed.
+If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
+is also given, then the number of variables preceding @code{WITH}
+must be the same as the number following it.
+In this case, tests for each respective pair of variables are
+performed.
+If the @code{WITH} keyword is given, but the
+@code{(PAIRED)} keyword is omitted, then tests for each combination
+of variable preceding @code{WITH} against variable following
+@code{WITH} are performed.
+
+@node WILCOXON
+@subsection Wilcoxon Matched Pairs Signed Ranks Test
+@vindex WILCOXON
+@cindex wilcoxon matched pairs signed ranks test
+
+@display
+ [ /WILCOXON @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
+@end display
+
+The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
+variables listed.
+The test does not make any assumptions about the variances of the samples.
+It does however assume that the distribution is symetrical.
+
+If the @subcmd{WITH} keyword is omitted, then tests for all
+combinations of the listed variables are performed.
+If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
+is also given, then the number of variables preceding @subcmd{WITH}
+must be the same as the number following it.
+In this case, tests for each respective pair of variables are
+performed.
+If the @subcmd{WITH} keyword is given, but the
+@subcmd{(PAIRED)} keyword is omitted, then tests for each combination
+of variable preceding @subcmd{WITH} against variable following
+@subcmd{WITH} are performed.
+
+@node T-TEST