Fitting data into a required function

I am trying to fit this data -

x = [0.001 0.01 0.1 1 10];
y = [63.5 74.5 94.1 94.2 103.5];

into a function y = m*x^n

polyfit and lsqcurvefit not seem to be working here

can someone help me?

Answer 1

[Star Strider]

Try this:

x = [0.001 0.01 0.1 1 10];
y = [63.5 74.5 94.1 94.2 103.5];

fcn = @(b,x) b(1).*x.^b(2);

B0 = rand(2,1);
B = lsqcurvefit(fcn, B0, x, y)

figure
plot(x, y, 'p')
hold on
plot(x, fcn(B,x), '-r')
hold off
set(gca, 'XScale','log')                                                % Optional
grid
xlabel('X')
ylabel('Y')
legend('Data', sprintf('y = %.3f\\cdotx^{%.3f}', B), 'Location','E')

.

[Vishal Dolas]

Thanks a lot it worked!!

[Star Strider]

As always, my pleasure!

Answer 2

[randerss simil]

%if true
 m = 2
 n = 1.5
 Y = m*x.^n;
 k = polyval(x,Y)

First use polyval for the equation and then apply polyfit