Covariance and Correlation
Covariance
Click on (Data, Bivariate Prototypes) and impose on the
vector display variables X [1 2 3 4 5] and Y[3 2 1 5 4]. Select
(Transformations, Deviations from the mean), click Select All and
the Append commands. Select
(Operations, Multiply Variables)
and click the Accept command.
You may also compute covariance directly by selecting
(Analysis I, Covariance), and marking variables X and Y
Correlation as the Standardized
Covariance
Transferring descriptors (Transfers,
Descriptors to the scalar module), Clearing (Clear All) the
scalar module display, selecting True Standard Deviation and
variables X and Y, selecting (Analysis I, Covariance)
and sending covariance to Cell 3
you can demonstrate definition of correlation as the
standardized covariance.
Correlation
Click on (Data, Bivariate Prototypes) and impose on the
vector display variables X [1 2 3 4 5] and Y[3 2 1 5 4]. Select
(Transformations, Standard Z Scores), mark the variables and click the Append
command. Select
(Operations, Multiply Variables)
and click the Accept command.
You may also compute correlation directly by selecting
(Analysis I, Coefficients of Correlation)
Clicking on the Correlation, Determination and
Alienation commands,
values of .50, .25, and .75 will be transferred to the Scalar module, as
By summing the coefficients of determination and
alienation, you can demonstrate that their sum equals to one.
