Linear regression with white Gaussian noise
η is an error term with distribution (0, sigma). The error or residual is how the true x differ from the estimated function.

Do you mean the theta when you say „parameter vector“. 

In this case, the estimated function is: 
y_hat = -1 + 0.3x + 0.6x^2. 

So if you want to infer the training set, you need to generate x points, evenly scattered around the quadratic function (mean of error = 0) with a variance of 0.1.