Employing
non-full-rank
design
matrices
throughout,
this
text
provides
a
concise
yet
solid
foundation
for
understanding
basic
linear
models.
It
introduces
the
basic
algebra
and
geometry
of
the
linear
least
squares
problem,
before
delving
into
estimability
and
the
GaussMarkov
model.
After
presenting
the
statistical
tools
of
hypothesis
tests
and
confidence
intervals,
the
author
analyzes
mixed
models,
such
as
two-way
mixed
ANOVA,
and
the
multivariate
linear
model.
The
text
presents
proofs
and
discussions
from
both
algebraic
and
geometric
viewpoints
and
includes
exercises
of
varying
levels
of
difficulty
at
the
end
of
each
chapter. Citește tot Restrânge
