We have been finding a set of optimal parameters for the linear least-squares regression minimization problem by identifying critical points, i. e. points at which the gradient of a # function is the zero vector, of the following function: F(Qo, ... ,Qm) = EN=1(Q, + Q7Xnı + ... + QmXnM - yn)?.
Please help to justify this methodology in the following way. Letting G: Rd - R be any function that is differentiable everywhere, show that, if G has a local minimum at a EB point xo, then its gradient is the zero vector there, i. e., VG(X) = 0.

Quick! a survey of 57 customers was taken at a bookstore regarding the types of books purchased. the survey found that 33 customers purchased mysteries, 25 purchased science fiction, 18 purchased romance novels, 12 purchased mysteries and science fiction, 9 purchased mysteries and romance novels, 6 purchased science fiction and romance novels, and 2 purchased all three types of books. a) how many of the customers surveyed purchased only mysteries? b) how many purchased mysteries and science fiction, but not romance novels? c) how many purchased mysteries or science fiction? d) how many purchased mysteries or science fiction, but not romance novels? e) how many purchased exactly two types of books?

Which equation represents the function on the graph?

Evaluate the expression. (9 + 5)(3 + 2)0