Normalize a feature in this table

Question

…use both feature scaling (dividing by the
“max-min”, or range, of a feature) and mean normalization.

So for any individual feature f:

f_norm = (f - f_mean) / (f_max - f_min)

e.g. for x2,(midterm exam)^2 = {7921, 5184, 8836, 4761}

> x2 <- c(7921, 5184, 8836, 4761)
> mean(x2)
 6676
> max(x2) - min(x2)
 4075
> (x2 - mean(x2)) / (max(x2) - min(x2))
 0.306  -0.366  0.530 -0.470

Hence norm(5184) = 0.366

(using R language, which is great at vectorizing expressions like this)

I agree it’s confusing they used the notation x2 (2) to mean x2 (norm) or x2′

EDIT: in practice everyone calls the builtin scale(...) function, which does the same thing.