In reply to Abbas.

Two assumptions for ordinary least squares regression are:
1. The X values are design points and are measured without error.
Clearly, that is not true here. If you draw a new sample, both the X and Y values will change, so there is uncertainty in the X direction.
2. The Y values are a function of the X variables plus independent (iid) normally distributed errors.
This is also not true for a Q-Q plot. The Y values are the empirical quantiles. The X values are the corresponding quantiles of some theoretical distribution. It is not true that Y = b0 + b1*X + eps, where eps ~ N(0,sigma) is a normal random variable. In fact, if you misspecify the distribution, the markers will not lie along a straight line.