We examine the question whether the dimension $D$ of a set
or probability measure is the same as the dimension of its
image under a typical smooth function, if the range space
is at least $D$-dimensional. If $\mu$ is a Borel
probability measure of bounded support in ${\Bbb R}^n$
with correlation dimension $D$, and if $m\geq D$, then
under almost every continuously differentiable function
(‘almost every’ in the sense of prevalence) from ${\Bbb
R}^n$ to ${\Bbb R}^m$, the correlation dimension of the
image of $\mu$ is also $D$. If $\mu$ is the invariant
measure of a dynamical system, the same is true for almost
every delay coordinate map. That is, if $m\geq D$, then
$m$ time delays are sufficient to find the correlation
dimension using a typical measurement function. Further,
it is shown that finite impulse response (FIR) filters do
not change the correlation dimension. Analogous theorems
hold for Hausdorff, pointwise, and information dimensions.
We show by example that the conclusion fails for
box-counting dimension.