@caption {Running two @cmd{DESCRIPTIVES} commands, one with the @subcmd{SAVE} subcommand}
@end float
+@float Screenshot, descriptives:scr
+@psppimage {descriptives}
+@caption {The Descriptives dialog box with two variables and Z-Scores option selected}
+@end float
+
In @ref{descriptives:res}, we can see that there are 40 valid data for each of the variables
and no missing values. The mean average of the height and temperature is 16677.12
and 37.02 respectively. The descriptive statistics for temperature seem reasonable.
by default, several statistics are calculated. Some are not particularly useful
for categorical variables, so you may want to disable those.
+@float Screenshot, frequencies:scr
+@psppimage {frequencies}
+@caption {The frequencies dialog box with the @exvar{sex} and @exvar{occupation} variables selected}
+@end float
+
From @ref{frequencies:res} it is evident that there are 33 males, 21 females and
2 persons for whom their sex has not been entered.
There is only one test variable, @i{viz:} @exvar{sex}. The other variables in the dataset
are ignored.
+@float Screenshot, chisquare:scr
+@psppimage {chisquare}
+@caption {Performing a chi-square test using the graphic user interface}
+@end float
+
In @ref{chisquare:res} the summary box shows that in the sample, there are more males
than females. However the significance of chi-square result is greater than 0.05
--- the most commonly accepted p-value --- and therefore
@caption {Running a one sample T-Test after excluding all non-positive values}
@end float
+@float Screenshot, one-sample-t:scr
+@psppimage {one-sample-t}
+@caption {Using the One Sample T-Test dialog box to test @exvar{weight} for a mean of 76.8kg}
+@end float
+
+
@ref{one-sample-t:res} shows that the mean of our sample differs from the test value
by -1.40kg. However the significance is very high (0.610). So one cannot
reject the null hypothesis, and must conclude there is not enough evidence
The null hypothesis is that both males and females are on average
of equal height.
+@float Screenshot, independent-samples-t:scr
+@psppimage {independent-samples-t}
+@caption {Using the Independent Sample T-test dialog, to test for differences of @exvar{height} between values of @exvar{sex}}
+@end float
+
+
In this case, the grouping variable is @exvar{sex}, so this is entered
as the variable for the @subcmd{GROUP} subcommand. The group values are 0 (male) and
1 (female).
If you are running the proceedure using syntax, then you need to enter
the values corresponding to each group within parentheses.
-
+If you are using the graphic user interface, then you have to open
+the ``Define Groups'' dialog box and enter the values corresponding
+to each group as shown in @ref{define-groups-t:scr}. If, as in this case, the dataset has defined value
+labels for the group variable, then you can enter them by label
+or by value.
+
+@float Screenshot, define-groups-t:scr
+@psppimage {define-groups-t}
+@caption {Setting the values of the grouping variable for an Independent Samples T-test}
+@end float
From @ref{independent-samples-t:res}, one can clearly see that the @emph{sample} mean height
is greater for males than for females. However in order to see if this
In this case, all variables in the data set are used. So we can use the special
keyword @samp{ALL} (@pxref{BNF}).
+@float Screenshot, reliability:src
+@psppimage {reliability}
+@caption {Reliability dialog box with all variables selected}
+@end float
+
@ref{reliability:res} shows that Cronbach's Alpha is 0.11 which is a value normally considered too
low to indicate consistency within the data. This is possibly due to the small number of
survey questions. The survey should be redesigned before serious use of the results are