Open the file Utilities.xls
in Microsoft Excel,
which gives data on 22 public utilities in the US.
Refer to Overview for details
about the variables mentioned above.
In
XLMiner™, select Data Reduction and Exploration
--> Principal
Components. In the Principal
Components dialog
box, select the
range of data to be analyzed (in this case,
XLMiner™
picks up the
appropriate range from the active data file). Select variables x1 to x8 and
move them to the Selected variables box with the transfer (>) button. The
figure below shows the first dialog box of
Principal Components Analysis.
Select the following options for step 2 of 3.
There are two options for specifying the number
of principal components:
Fixed #components : You can specify a fixed
number here.
Smallest #components explaining
: This option lets you specify a percentage, and
XLMiner™
will calculate the minimum number of principal components required to account
for that percentage of variance. Do not select it here.
Method
: Use the default setting as shown above.
- In the third dialog box, check the outputs to be displayed as shown in
the figure below, and click on Finish.
Show standardized principal components:
This option causes each principal component to be divided by the square root of
its variance.
Show principal components
score: This option results in the display of a matrix in which the
columns are the principal components, the rows are the individual data records,
and the value in each cell is the calculated score for that record on the
relevant principal component.
- The output is displayed on separate sheets and various sections can be viewed by
using the Output Navigator. The figures below show various outputs.
