Question regarding $O(n^2)$ efficiency I'm going through a video of EDX course which talks about Big O notation. At the end of the video they have some questions but the $O(n^2)$ answer is confusing me. It feels like a mistake, but I just want to make sure.
The question is :

Imagine that we have a data set of 10 items. We run an algorithm on that data set, and it performs 10 operations.
Now imagine that we doubled the size of the data set to 20 items. Approximately how many operations might now be required if the algorithm is of...

Linear order? 20

Constant Order? 10

Quadratic Order? 40



I don't understand why the answer if 40 if it's quadratic.
At first I thought it would be 400, because n2 is 400. But the drop-down answer menu doesn't have 400. The highest it has is 100.
So I thought it might be 100, because if 10 items take 100 operations, then increasing by 10, would increase by 100. However 100 seems to be wrong as well.
So why is the answer 40?
The course in question is here:
https://www.edx.org/course/introduction-computing-using-python-gtx-cs1301x
Chapter 5.2
Say the time spent is some constant factor $c$ times $n^2$. Then we have:

$$c \cdot 10^2 = 10$$
$$c = 0.1$$

Thus then $c\cdot 20^2 = 40$.