Why is the variance of

Var(b̂) = σ2 / Inner product of (Z)

where Z is the residual vector of Gram-Schmidt process.

I am learning it from the book

Elements of Statistical Learning (Data Mining,Inference, and Prediction)

on page 55 chapter 3

I was reading about variance of estimators and i saw

Var(b̂) = σ2 / Inner product of (Z)

where Z is the residual vector of Gram-Schmidt process. I do not understand how they were able to get the variance of the coefficient

I am learning it from the book

Elements of Statistical Learning (Data Mining,Inference, and Prediction)

on page 55 chapter 3