* EXAMINE:: Testing data for normality.
* CORRELATIONS:: Correlation tables.
* CROSSTABS:: Crosstabulation tables.
-* FACTOR:: Factor analysis and Principal Components analysis
+* FACTOR:: Factor analysis and Principal Components analysis.
+* LOGISTIC REGRESSION:: Bivariate Logistic Regression.
* MEANS:: Average values and other statistics.
* NPAR TESTS:: Nonparametric tests.
* T-TEST:: Test hypotheses about means.
The @subcmd{STATISTICS} subcommand specifies the statistics to be displayed:
@table @code
-@item ALL
+@item @subcmd{ALL}
All of the statistics below.
-@item MEAN
+@item @subcmd{MEAN}
Arithmetic mean.
-@item SEMEAN
+@item @subcmd{SEMEAN}
Standard error of the mean.
-@item STDDEV
+@item @subcmd{STDDEV}
Standard deviation.
-@item VARIANCE
+@item @subcmd{VARIANCE}
Variance.
-@item KURTOSIS
+@item @subcmd{KURTOSIS}
Kurtosis and standard error of the kurtosis.
-@item SKEWNESS
+@item @subcmd{SKEWNESS}
Skewness and standard error of the skewness.
-@item RANGE
+@item @subcmd{RANGE}
Range.
@item MINIMUM
Minimum value.
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 any @cmd{/VARIABLES} subcommand
The @subcmd{/PRINT} subcommand may be used to select which features of the analysis are reported:
-@itemize @subcmd{}
-@item UNIVARIATE
+@itemize
+@item @subcmd{UNIVARIATE}
A table of mean values, standard deviations and total weights are printed.
-@item INITIAL
+@item @subcmd{INITIAL}
Initial communalities and eigenvalues are printed.
-@item EXTRACTION
+@item @subcmd{EXTRACTION}
Extracted communalities and eigenvalues are printed.
-@item ROTATION
+@item @subcmd{ROTATION}
Rotated communalities and eigenvalues are printed.
-@item CORRELATION
+@item @subcmd{CORRELATION}
The correlation matrix is printed.
-@item COVARIANCE
+@item @subcmd{COVARIANCE}
The covariance matrix is printed.
-@item DET
+@item @subcmd{DET}
The determinant of the correlation or covariance matrix is printed.
-@item KMO
+@item @subcmd{KMO}
The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
-@item SIG
+@item @subcmd{SIG}
The significance of the elements of correlation matrix is printed.
-@item ALL
+@item @subcmd{ALL}
All of the above are printed.
-@item DEFAULT
+@item @subcmd{DEFAULT}
Identical to @subcmd{INITIAL} and @subcmd{EXTRACTION}.
@end itemize
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
projects / pspp / blobdiff