Comparing least square equations in matlab

[honda221]

The function should accept the input arrays x and y and to process the data using
linear, semilogy, and log‐log least squares fits to determine which is the best. For this problem, the "best" is given by the smallest value. I wrote the individual scripts for each for the different equations, but I wasn't sure how to compare them to each other in Matlab. Any help would be appreciated!

[a,b,type] = linear_ls2_xxx(x,y)
% This function creates returns the linear parameters of the
% slope (a) and y-intercept (b) given a set of input data (x,y).
% The function attempts to curve fit to linear, semilogy and log-log
% using natural logarithms.
% The returned values indicate those with the smallest sum of squared
% error.
% Calling parameters
%... (you fill in)
% Returned parameters
%... (you fill in)
% type flag indicating which type of equation had the best fit
% type=1 for linear, =2 for semilogy, =3 for log-log

 

[lippyka]

% [c,d,type] = semilogy_ls2_xxx(x,y) % This function creates returns the linear parameters of the % slope (c) and y-intercept (d) given a set of input data (x,y). % The function attempts to curve fit to linear, semilogy and log-log % using natural logarithms. % The returned values indicate those with the smallest sum of squared % error. % Calling parameters %... (you fill in) % Returned parameters %... (you fill in) % type flag indicating which type of equation had the best fit % type=1 for linear, =2 for semilogy, =3 for log-log [e,f,type] = loglog_ls2_xxx(x,y) % This function creates returns the linear parameters of the % slope (e) and y-intercept (f) given a set of input data (x,y). % The function attempts to curve fit to linear, semilogy and log-log % using natural logarithms. % The returned values indicate those with the smallest sum of squared % error. % Calling parameters %... (you fill in) % Returned parameters %... (you fill in) % type flag indicating which type of equation had the best fit % type=1 for linear, =2 for semilogy, =3 for log-log % Initialize bestFit and typebestFit = Inf;type = 0;% Run all three fits and compare results[a,b,fitType] = linear_ls2_xxx(x,y);if a*b < bestFit bestFit = a*b; type = fitType;end[c,d,fitType] = semilogy_ls2_xxx(x,y);if c*d < bestFit bestFit = c*d; type = fitType;end[e