Couple comments:
Curve Fitting Toolbox includes a lot of nice code that will automatically choose "good" starting points for nonlinear regressions for supported model types. You're much better off using the library of standard models rather than hard coding your own model.
One of the nice things about cftool is that you can use the tool to generate a fit and then (automatically) create a function that with replicate this analysis without any need to open the GUI.
I attached some code that I auto generated from cftool.
The function had no trouble generating a good fit. I used a "power" model which looks to be the same as your custom equation.
function [fitresult, gof] = createFit1(X, Y)
%CREATEFIT1(X,Y)
% Create a fit.
%
% Data for 'untitled fit 1' fit:
% X Input : X
% Y Output: Y
% Output:
% fitresult : a fit object representing the fit.
% gof : structure with goodness-of fit info.
%
% See also FIT, CFIT, SFIT.
% Auto-generated by MATLAB on 09-Jan-2012 09:08:03
%%Fit: 'untitled fit 1'.
[xData, yData] = prepareCurveData( X, Y );
% Set up fittype and options.
ft = fittype( 'power1' );
opts = fitoptions( ft );
opts.Display = 'Off';
opts.Lower = [-Inf -Inf];
opts.StartPoint = [1.92785231569241e-005 -1.5782710696971];
opts.Upper = [Inf Inf];
% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h = plot( fitresult, xData, yData );
legend( h, 'Y vs. X', 'untitled fit 1', 'Location', 'NorthEast' );
% Label axes
xlabel( 'X' );
ylabel( 'Y' );
grid on
