= Approximate experimental design
{title2=$\xi=\sum_iw_i\delta_{x_i}$}
An approximate design is a <probability measure> on the design region, with weight $w_i$ specifying a proportion of observations at $x_i$. For $N$ independent observations and common error <variance> $\sigma^2$, a <linear regression> with vector $f(x)$ has normalized <information matrix of an experimental design> $M(\xi)=\int f(x)f(x)^T\,d\xi(x)$; its <least-squares estimator> has <covariance> $\sigma^2M(\xi)^{-1}/N$ when the allocations realize these proportions. Exact designs restrict $Nw_i$ to integers.
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