# Standard error of components of coefficient estimate in linear regression

As I understand it, $$\text{standard error} = \frac{\text{standard deviation}}{\sqrt{n}}$$, where $$n$$ is the number of observations. I also know that $$cov(\hat{\beta}) = \sigma^{2}(X^{T}X)^{-1}$$; this is a pxp variance-covariance matrix. So why is the standard error of the components of the coefficient estimate vector $$\boldsymbol{\hat{\beta}}$$ in, for example, a normal linear model, $$\sigma^{2}[(X^{T}X)^{-1}]_{ii}$$ and not $$\frac{1}{\sqrt{n}}\sigma^{2}[(X^{T}X)^{-1}]_{ii}$$ where the subscript $$ii$$ denotes the $$i,i$$th entry of the matrix?

• i asked a related question yesterday, using R... stats.stackexchange.com/questions/262160/… – Haitao Du Feb 16 '17 at 21:28
• The "$n$" factor is implicit already: consider the case where $X$ has been whitened, so $X^TX=nI$. – GeoMatt22 Feb 16 '17 at 21:35
• @GeoMatt22 Hmm, yes, I can see when it is whitened it works but I can't generalise it - could you elaborate on where it is implicit? – python_learner Feb 16 '17 at 21:57
• It's just dot products vs. covariances (sums vs. averages): $\boldsymbol{x}^T\boldsymbol{y} = \overline{(xy)}\times{n}$ – GeoMatt22 Feb 16 '17 at 22:21
• @GeoMatt22 I'm afraid I don't find it any easier to understand using the equality you presented. :l Could you provide working? – python_learner Feb 16 '17 at 22:54

The "$n$" factor is implicit already in $\boldsymbol{X}^T\boldsymbol{X}$.
This is easiest to see in the case where $\boldsymbol{X}$ has been whitened, so $\boldsymbol{X}^T\boldsymbol{X}=n\boldsymbol{I}$.
More generally we have $\boldsymbol{X}=[\boldsymbol{x}_1,\ldots\boldsymbol{x}_p]$, where $\boldsymbol{x}_p\in\mathbb{R}^n$. Then the entries of the matrix are $$\left(\boldsymbol{X}^T\boldsymbol{X}\right)_{ij}=\boldsymbol{x}_i^T\boldsymbol{x}_j$$ Now choose a pair of columns, $\boldsymbol{a}=\boldsymbol{x}_i$ and $\boldsymbol{b}=\boldsymbol{x}_j$. Then we have $$\boldsymbol{a}^T\boldsymbol{b}=\sum_{k=1}^na_kb_k=\overline{(ab)}\,n$$ So each entry of the matrix is a dot product, which can be seen as $n$ times an average.
In the case where $\boldsymbol{X}$ has been centered so the columns have zero means, the product-matrix entries are covariances.