+The default is @subcmd{LISTWISE}.
+
+@node GLM
+@section GLM
+
+@vindex GLM
+@cindex univariate analysis of variance
+@cindex fixed effects
+@cindex factorial anova
+@cindex analysis of variance
+@cindex ANOVA
+
+
+@display
+GLM @var{dependent_vars} BY @var{fixed_factors}
+ [/METHOD = SSTYPE(@var{type})]
+ [/DESIGN = @var{interaction_0} [@var{interaction_1} [... @var{interaction_n}]]]
+ [/INTERCEPT = @{INCLUDE|EXCLUDE@}]
+ [/MISSING = @{INCLUDE|EXCLUDE@}]
+@end display
+
+The @cmd{GLM} procedure can be used for fixed effects factorial Anova.
+
+The @var{dependent_vars} are the variables to be analysed.
+You may analyse several variables in the same command in which case they should all
+appear before the @code{BY} keyword.
+
+The @var{fixed_factors} list must be one or more categorical variables. Normally it
+will not make sense to enter a scalar variable in the @var{fixed_factors} and doing
+so may cause @pspp{} to do a lot of unnecessary processing.
+
+The @subcmd{METHOD} subcommand is used to change the method for producing the sums of
+squares. Available values of @var{type} are 1, 2 and 3. The default is type 3.
+
+You may specify a custom design using the @subcmd{DESIGN} subcommand.
+The design comprises a list of interactions where each interaction is a
+list of variables separated by a @samp{*}. For example the command
+@display
+GLM subject BY sex age_group race
+ /DESIGN = age_group sex group age_group*sex age_group*race
+@end display
+@noindent specifies the model @math{subject = age_group + sex + race + age_group*sex + age_group*race}.
+If no @subcmd{DESIGN} subcommand is specified, then the default is all possible combinations
+of the fixed factors. That is to say
+@display
+GLM subject BY sex age_group race
+@end display
+implies the model
+@math{subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race}.
+
+
+The @subcmd{MISSING} subcommand determines the handling of missing
+variables.
+If @subcmd{INCLUDE} is set then, for the purposes of GLM analysis,
+only system-missing values are considered
+to be missing; user-missing values are not regarded as missing.
+If @subcmd{EXCLUDE} is set, which is the default, then user-missing
+values are considered to be missing as well as system-missing values.
+A case for which any dependent variable or any factor
+variable has a missing value is excluded from the analysis.
+
+@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.