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** Index**

Many people regard journal articles and books that contain matrix
algebra as prohibitively complicated and ignore them or shelve them
indefinitely. This is a sad state of affairs because learning matrix
algebra is not difficult and can reap enormous benefits. Science in
general, and genetics in particular, is becoming increasingly
quantitative. Matrix algebra provides a very economical language to
describe our data and our models; it is essential for understanding Mx
and other data analysis packages. In common with most languages, the
way to make it ``stick'' is to *use* it. Those unfamiliar with,
or out of practice at, using matrices will benefit from doing the
worked examples in the text. Readers with a strong mathematics
background may skim this chapter, or skip it entirely, using it for
reference only. We do not give an exhaustive treatment of matrix
algebra and operations but limit ourselves to the bare essentials
needed for structural equation modeling. There are many excellent
texts for those wishing to extend their knowledge; we recommend Searle
(1982) and Graybill (1969).
In this chapter, we will introduce matrix notation in Section 4.2
and matrix operations in Section 4.3. The general use of
matrix algebra is illustrated in Section 4.4 on equations and
Section 4.5 on other applications.

** Next:** 2 Matrix Notation
** Up:** 4 Matrix Algebra
** Previous:** 4 Matrix Algebra
** Index**
Jeff Lessem
2002-03-21