# Linear regression with white Gaussian noise

I am new to machine learning , so this question may sound fundamental. My task is to estimate the parameter vector of the equation with the least squares method:

$$y = \theta_0 + \theta_1x + \theta_2x^2 + η$$

Where η corresponds to white Gaussian noise with mean 0 and variance 0.1

Also , I have been given the prior values of the parameter vector , say [-1,0.3,0.6] . I have to generate the N points of the training set .

Should regresor be like this :

[1 0.1 (o.1)^2] (for x = 0.1)


And should I calculate a priori results by adding noise