Question2 (no need for coding for Question2]: Suppose we have a dataset with features: Xi-GPA, X2 = Age, X3 -Type of Position (1 for Technical positions, and 0 fo Non-Technical positions), and we have built a non-linear regression model as: The prediction target is "starting salary after graduation" (in thousands of dollars). Suppose we train (fit) the model, and get -30, 1-20, 2-0.07, ,--30, ,-0.01, 5-10. (a) Which answer is correct, and why? i. For a fixed value of Age and GPA, Technical positions earn more on average than non- technical positions. ii. For a fixed value of Age and GPA, Non-Technical positions earn more on average than Technical positions. ii. For a fixed value of Age and GPA, Technical positions earn more on average than Non-Technical positions when the GPA is high enough. iv. For a fixed value of Age and GPA, Non-Technical positions earn more on average thar Technical positions when the GPA is high enough. (b) Predict the salary of a Technical and a Non-Technical positions with Age of 27, GPA of 4.0