R's linear model summary object has a unscaled variance feature, which appears to be what is calculated when solve(t(X)%*%X)*sigma^2 is calculated directly. What makes this "unscaled" ? What is the alternative?
what is the "unscaled variance" in r's linear model summary?
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What makes it "unscaled" is that it's not scaled by the estimated variance
sigma^2
, that is:solve(t(X) %*% X)
whereX
refers to the design-matrix. This is in contrast to the (scaled) variance of the coefficients:solve(t(X) %*% X)*sigma^2
.If you need the scaled variance, i.e.
solve(t(X) %*% X)*sigma^2
, then you can simply scale it or usevcov()
. A small example follows: