Feature scaling and mean normalization [closed]

I’m taking Andrew Ng’s machine learning course and was unable to get the answer to this question correct after several attempts. Kindly help solve this, though I’ve passed through the level.

Suppose $m=4$ students have taken some class, and the class had a midterm exam and a final exam. You have collected a dataset of their scores on the two exams, which is as follows:

midterm (midterm)^2   final
89        7921        96
72        5184        74
94        8836        87
69        4761        78


You’d like to use polynomial regression to predict a student’s final exam score from their midterm exam score. Concretely, suppose you want to fit a model of the form $h_\theta(x) = \theta_0 + \theta_1 x_1 + \theta_2 x_2$, where $x_1$ is the midterm score and $x_2$ is (midterm score)^2. Further, you plan to use both feature scaling (dividing by the “max-min”, or range, of a feature) and mean normalization.

What is the normalized feature $x_2^{(4)}$? (Hint: midterm = 89, final = 96 is training example 1.) Please enter your answer in the text box below. If applicable, please provide at least two digits after the decimal place.

1. $x^{(4)}_2 \to 4761$.
2. Nomalized feature $\to \dfrac{x - u}{s}$ where $u$ is average of $X$ and $s = max - min = 8836 - 4761 = 4075$.
3. Finally, $\dfrac{4761 - 6675.5}{4075} = -0.47$