I'm attaching code that shows a couple different ways to solve your problem.
I prefer the second option. The R^2 is slightly better and the model is simplier. (Note that option 2 as coded uses Statistics Toolbox rather than Curve Fitting Toolbox)
%%Input some data
x = [0 10 20 30 35 40 45 50 55 60];
y = [60 120 180 240 300];
z = [299.8 299.1 296.9 293.4 291.4 289.9 288.5 283.3 276.4 272.1;
299.8 299.1 296.9 293.4 291.4 289.9 288.5 283.3 276.4 272.1;
299.8 299.1 296.9 293.4 291.4 289.9 288.5 283.3 276.4 272.1;
299.8 299.1 296.9 293.4 291.4 289.9 288.5 283.3 276.4 272.1;
299.8 299.1 296.9 293.4 291.4 289.9 288.5 283.3 276.4 272.1];
% Observe that the data is constant in one dimension.
% The variable Y has no predictive power
% This means we can treat this as a curve fitting problem rather
% than a surface fitting problem
x = x';
z = mean(z, 1)';
% Visualize our data
scatter(x,z)
%%Generate a fit
% Option one
[foo, GoF] = fit(x,z, 'poly2')
hold on
plot(foo)
%%Option two: Fit a piecewise linear model
% Looking at the data, it appears as if we might generate a better fit by
% assuming a piecewise linear model with a "kink" at the 7th data point
% Start by centering the data such that the 7th data point is equal to zero
New_x = x - 45;
% Create a dummy variable that flags whether we are before or after the 7th
% data point
dummy = New_x > 0
% Create an interaction term
interaction = New_x .* dummy
% Generate our regression model
X = [ones(size(x)), New_x, interaction];
b = regress(z, X)
plot(x, X*b)
