Hello there, can you help me solve the problem with two subparts (b and c)Problem: (the image is attached)Subparts:b. Find the least-squares curve of the form above to fit the data (4, 1.57), (6, 2.05), (8, 2.5), (10, 2.7), (12, 3.1), (14, 3.6), (16, 3.8), and (18, 4.35), where x and y represent sales and costs in thousands. Produce a graph that shows the data pts and the graph of the cubic approximation.y = __x + (__)x2 + (__)x3 c. (the image is attached) c. If a machine learned the curve you found in (b), what output would it provide for aninput of x = 13? A simple curve that often makes a good model for the variable cost of a company, as a2function of the sales level x, has the form y =B₁x + ß₂×² + ß3×³. There is no constant termbecause fixed costs are not included. Complete parts (a) through (c).

(b)Given the the data points .We need to fit the curve of the form y=β1x+β2x2+β3x3.The normal…

Find the least-squares curve of the form above to fit the data (4, 1.57), (6, 2.05), (8, 2.5), (10, 2.7), (12, 3.1), (14, 3.6), (16, 3.8), and (18, 4.35), where x and y represent sales and costs in thousands. Produce a graph that shows the data pts and the graph of the cubic approximation.

Find the least-squares curve of the form above to fit the data (4, 1.57), (6, 2.05), (8, 2.5), (10, 2.7), (12, 3.1), (14, 3.6), (16, 3.8), and (18, 4.35), where x and y represent sales and costs in thousands. Produce a graph that shows the data pts and the graph of the cubic approximation.

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Hello there, can you help me solve the problem with two subparts (b and c)? Thank you Problem: (the image is attached) Subparts: b. Find the least-squares curve of the form above to fit the data (4, 1.57), (6, 2.05), (8, 2.5), (10, 2.7), (12, 3.1), (14, 3.6), (16, 3.8), and (18, 4.35), where x and y represent sales and costs in thousands. Produce a graph that shows the data pts and the graph of the cubic approximation. y = __x + (__)x2 + (__)x3 (round to six decimal places as needed.) c. (the image is attached) (round to four decimal places)
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