HW8 - 17.2 (a) 3.500167731 (b) 1.99320, t t = 42.88% (c) n...

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Problem Instructions and changes in problems 17.2 Part (f) is single application of Simpson’s 3/8 rule. 17.5 For part (c) use combination of trapezoidal and Simpon’s rule that gives the highest accuracy. 17.6 18.3 Review Section 18.2 for error calculation. Don't forget to change the integral bounds to 1 and 1. Use format long or long e to observe the difference in methods. 19.1 Use the finite differences formulas in Figs. 19.3, 19.4, 19.5 19.4 Use only one application of Richardson’s extrapolation. 19.9 Your Matlab program should do the following: 1) Take x and y values as inputs. 2) Spline fit the data points using the built-in Matlab function spline at a 1001 equally spaced points between 0.6 and 3.5. 3) Calculate the derivatives with the gradient function. 4) Extract the derivatives at the data points.
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Unformatted text preview: 17.2 (a) 3.500167731 (b) 1.99320, t t = 42.88% (c) n = 2, 2.96303, t t = 15.35% n = 4, 3.3437, t t = 4.47% (d) 3.28427, t t = 6.17% (e) 3.47059, t t = 0.84% (f) 3.388365, t t = 3.19% 17.5 (a) 0.79124 (b) 0.79284, t t = 0.2022% (c) 0.791282, t t = 0.0052% 17.6 (a) 2.666667 (b) 2, t = 25% (c) 2.666667, t = 0% 18.3 (a) 41.21305531, a = 0.3579%, t = 0.102% (b) 39.6075058, t = 3.8% (c) quad solution 41.17107385090233, t = 1.1e-8% quadl solution 41.17107466800178, t = 2e-6% 19.1 19.4 D = -0.70539 19.9 True derivative values = -0.3012 -0.4979 -0.4484 -0.1348 -0.0274 Spline fit derivative values = -0.5873 -0.4952 -0.4496 -0.1286 -0.0581...
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HW8 - 17.2 (a) 3.500167731 (b) 1.99320, t t = 42.88% (c) n...

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