Microsoft Office Tutorials and References

In Depth Information

Exhibit 3.27
Covariance matrix for product A–E

covariance like those for product A and B (and C also) indicate little co-variation.

The problem with this analysis is that it is not a simple matter to know what we

mean by large or small values—large or small relative to what.

Fortunately, statisticians have a solution for this problem—
Correlation
analysis.

Correlation
analysis will make understanding the linear co-variation or co-relation

between two variables much easier, because it is measured in values that are stan-

dardized between the range of –1 and 1. A correlation coefﬁcient of 1 for two

data series indicates that the two series are
perfectly positively correlated
:as

one variable increases so does the other. If correlation coefﬁcient of –1 is found,

then the series are
perfectly negatively correlated
: as one variable increases the

other decreases. Two series are said to be independent if their correlation is 0. The

calculation of correlation coefﬁcients involves the covariance of two data series.

In Exhibit 3.28 we see a correlation matrix which is very similar the covariance

matrix. You can see that the strongest positive correlation in the matrix is between

products D and E, 0.725793, and the strongest negative correlation is between A

and E, where the coefﬁcient of correlation is –0.65006. There are also some values

that indicate near linear independence; for example, products A and B with a coef-

ﬁcient of 0.118516. Clearly this is a more direct method of determining the linear

correlation of one data series with another than the covariance matrix.

Search JabSto ::

Custom Search