It is relatively easy with core MATLAB functions to do a nonlinear regression. See http://www.mathworks.com/matlabcentral/answers/171718-how-can-write-this-in-matlab#comment_262537 for a nonlinear regression of a function quite similar to yours.
The problem with log transformations, especially with only 5 data pairs, is that the additive, normally-distributed errors (that the least squares technique assumes) become log-normally distributed, giving potentially inaccurate parameter estimates.
Since ‘k’ is the y-intercept and ‘n’ is the slope, your objective function (replacing the ‘P’ function in my previous answer) and where k=b(1) and n=b(2) is:
m = @(b,P) b(1).*P.^b(2);
and the cost function becomes:
SSECF = @(b) sum((y - m(b,P)).^2);
If you absolutely must do a log-log regression, use polyfit:
B = polyfit(log(P), log(m), 1);
