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MTH 171 Lecture Notes - Lecture 21: Approximation Error, Linearization, Trigonometric Functions

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13 Apr 2017

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University of Miami

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Provide a generalization to each of the key terms listed in this section. L (x) f (a) (x a) + f (a) Let"s say that you have a function, which is normally f (x), that"s di erentiable at the a point, which can be labeled with a. When it comes to any estimation methods, then there is an error is actually being committed; the absolute error of a linear approximation is the exact estimation minus the approximated estimation, which can be explained the following: Approximation = f (a) (x a) + f (a) Ae = e (x) = exact approximation. Ae = e (x) = f (x) [f (a) (x a) + f (a)] If the estimation error, which is e(x), is greater than 0, which can be expressed with e(x) > 0, then that means that the approximation would be an underestimation.

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Related Questions

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answers the Qs? 5 stars for corect and complete answers

This exercise is intended to give you some insight into why the second derivative test works. A good pan of what I'm asking you to do has been adapted form the Discovery Project on page 821 of our text. In 11.4, we wrote down the tangent plane or linear approximation of a function f(x, y) at a point. To simplify this exercise, we're going to assume that the point is located at the origin, x=0, y=0. Then At a critical point, the two partial derivatives are 0, so the linear approximation just simplifies f(x, y) f(0,0), which is the equation of a horizontal plane. This just doesn't give enough information to determine whether we have a max, min, or saddle. So we look at the quadratic approximation at (0,0), which is defined as follows: At a critical point, this will reduce to It can be proved that if the function f(x, y) has continuous second partials, then it will have the same type of critical point as its quadratic approximation. Let f(x, y) = e-x2-2y2 Find the first partial derivatives Find the first partial derivatives fx(x, y) and fy(x, y): Evaluate fx(0,0) and fy(0, 0). Find the second partial derivatives fxx(x, y), fxy(x, y) and fyy(x, y): Evaluate fxx, fxy and fyy at the critical point. In order to recognize the shape of this graph from its equation, it will be helpful to rewrite the equation in a different form so that: Complete the square for the quadratic approximation and state the values of a, b, c, and d. What docs the graph of the quadratic approximation look like? A: paraboloid opening up. B: paraboloid opening down, C: saddle. Judging from your knowledge of the shape of the graph in exercise 16, does the quadratic approximation have a max, min or saddle? Enter MAX, MIN, or SADDLE. Recall that in the second derivative test, at a critical point (a. b). For f(x, y), state the values of D and fxx. Does the second derivative test tell you that the critical point of f is a local maximum, local minimum, or saddle? After you've completed the lab, we'll take time out to see how the quadratic approximation relates to the second derivative test.

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