Standardising a Held-Out Row

A single numeric feature is being prepared for a model. The rows have already been split, and the training set contains these eight values:

$72, \quad 58, \quad 80, \quad 66, \quad 74, \quad 64, \quad 76, \quad 70$

One further row was held out for validation. Its raw value is

$x_{\text{val}} = 88$

You now fit a Z-score standardizer

$z = \frac{x - \mu}{\sigma}$

following the golden rule of validation: the scaler is fitted on the training rows only, and the fitted scaler is then applied to the held-out row.

Use the sample standard deviation, dividing the sum of squared deviations by $n - 1$.

What standardized value does the fitted scaler produce for the held-out row?

Round your answer to 2 decimal places.