Es240_test2_solution_2_fall10-1 - %Problem 2%Obtain a blank...

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%Problem 2 %Obtain a blank slate clear all; close all; clc; x = [5 10 15 20 25 30 35 40 45 50] y = [17 24 31 33 37 37 40 40 42 41] %A. Linear transformations %a) employ transformations to linearize the data and use linear least-squares regression to fit the data into %i. a power model: yn = log(y); xn = log(x); p = polyfit(xn,yn,1) %linear coefficients obtained by MATLAB built-in ynest = polyval(p,xn); %predicted value for linear tranformation %Re-transforming to nonlinear b = p(1) a = exp(p(2)) %actual predicted y yestp = a*x.^b %ii. an exponential model %linear transformation lny = log(y) p = polyfit(x,lny,1) %linear coefficients obtained by MATLAB built-in lnyn = polyval(p,x); %predicted value for lineare tranformation %Re-transforming to nonlinear beta = p(1) %b = a1 alpha = exp(p(2)) %a = exp(a0) %actual predicted y yeste = alpha*exp(beta*x) %plotting the retransformed data figure plot(x,y,'g*', x, yestp,'m',x,yeste); title('Best fit line for re-transformed exponential model'); xlabel('x'); ylabel('y');
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This note was uploaded on 09/04/2011 for the course ENGINEERIN 240 taught by Professor Druche during the Spring '11 term at Montgomery College.

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Es240_test2_solution_2_fall10-1 - %Problem 2%Obtain a blank...

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