Question 2. (a) Use Python to compute the weight vector w = [w₀,w₁,w₂]^T for linear regression of N input data points (x_i,y_i) that minimizes the sum of squared error (w^Tx_i y_i)² (use the method in slide 29 in lecture 2). The input data are

x₁=(1,3,2)^T y₁ = 3

x₂=(1,2,2)^T y₂ = 1

x₃=(1,1,-1)^T y₃ = -1

x₄=(1,-1,-3)^T y₄= -3

The first element in vector x_i is always x₀=1 (See slides in lecture 1). Intermediate steps of the computation are needed (20 points). (b) Use the linearRegression module in Python package sklearn to find w for linear regression. Steps of the computation are needed (5 points).