Hello, help on this linear regression problem would be appreciated.

Regression Problem 9. Consider linear regression on a set of N data points with 2D input vectors (xi = [Xi,1, xi,2]) and continuous labels (yi). We hope to find a linear function f(Xi) = W1*Xi, 1 + W2*Xi,2 to fit label yi. A. Write down the sum of squared predicted error of this linear regression problem where your true label is yi, and the prediction is defined by f(Xi). B. Now formulate this linear regression problem as an optimization problem. Hint: minimize the sum of squared predicted error defined above. C. Derive the solution (w1 and w2). You can also show your answer using matrices and vectors