13.2 - Example: Sales Data
13.2 - Example: Sales DataExample 13-1: Sales
The example data comes from a firm that surveyed a random sample of n = 50 of its employees in an attempt to determine which factors influence sales performance. Two collections of variables were measured:
- Sales Performance:
- Sales Growth
- Sales Profitability
- New Account Sales
- Test Scores as a Measure of Intelligence
- Creativity
- Mechanical Reasoning
- Abstract Reasoning
- Mathematics
There are p = 3 variables in the first group relating to Sales Performance and q = 4 variables in the second group relating to Test Scores.
Download the text file containing the data here: sales.csv
Canonical Correlation Analysis is carried out in SAS using a canonical correlation procedure that is abbreviated as cancorr. Let's look at how this is carried out in the SAS Program below
Download the SAS program here: sales.sas or click on the copy icon inside Explore the Code.
Note: In the upper right-hand corner of the code block you will have the option of copying ( ) the code to your clipboard or downloading ( ) the file to your computer.
options ls=78;
title "Canonical Correlation Analysis - Sales Data";
data sales;
infile "D:\Statistics\STAT 505\data\sales.csv" firstobs=2 delimiter=',';
input growth profit new create mech abs math;
run;
/* The vprefix and wprefix options specify names to separate the two
* sets of variables for the analysis.
* The vname and wname options are more descriptive string names to be used
* to describe the two sets of variables.
* The var and with statements specify which variables go into each set.
* They are referred to by the terms used in the vprefix and wprefix, respectively.
*/
proc cancorr data=sales out=canout
vprefix=sales vname="Sales Variables"
wprefix=scores wname="Test Scores";
var growth profit new;
with create mech abs math;
run;
/* This plots the first canonical pair as a 2d scatterplot.
* Other canonical pairs can also be plotted by changing
* the variables used in the plot statement.
*/
proc gplot data=canout;
axis1 length=3 in;
axis2 length=4.5 in;
plot sales1*scores1 / vaxis=axis1 haxis=axis2;
symbol v=J f=special h=2 i=r color=black;
run;