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In the ANalysis Of VAriance we are interested in the mutual dependence
of a dependent variable with interval scale and independent variables
with nominal scale.       In the analysis of covariance a part of the
independent variables are at the interval scale. Usually the following
assumptions are made: The observational errors are independently and
normally distributed with equal variances.

With the ANOVA operation you may analyse a quite large range of variance
and covariance models. The general form of the ANOVA operation is

         ANOVA <data>,L
         DEPENDENT=<list of dependent variables> 
         <definitions for the grouping variables> 
         <list of covariates> 
         <definitions for analyses and tests to be performed> 

The parameter L (optional) gives the starting line for the results
in the edit field. At least one dependent variable must be given.
An example of the specifications for a two-way fixed effects analysis
of variance model:
         ANOVA IEADATA,30
         DEPENDENT=KNOWLDGE
         GROUPING=ATTITUDE,SEX
         ATTITUDE=1(Best),2(Same),3(Worst)  SEX=1(Boys),2(Girls)
Means and deviations will be automatically printed in one-sample
and one-way analysis of variance. In other analyses means and
correlations are printed only if PRINTOUT=MEANS is specified.
Further information:
  1 = Definitions for grouping variables 
  2 = One-sample tests 
  3 = One-way analysis of variance, multiple comparisons of means 
  4 = Analysis of variance for multiple factors 
  5 = Analysis of covariance 
  6 = Multivariate analysis of variance and covariance 
  7 = Multivariate analysis of repeated measurements 
  8 = Performing analyses in subgroups 
  9 = Forming combined grouping variables 
  I = Input in other forms (not data) 
  D = More on data analysis