use least squares to create a function that performs regression for v = a*(x/(x+b)) and output a, b, and r^2 in matlab

main.m
x = [x1, x2, x3, ..., xn]; % input independent variable data
v = [v1, v2, v3, ..., vn]; % input dependent variable data
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main.m
model = @(x, params) params(1) * (x ./ (x + params(2)));
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main.m
initialGuess = [initialA, initialB];
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main.m
params = lsqcurvefit(model, initialGuess, x, v);
a = params(1);
b = params(2);
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main.m
% Calculate the predicted values using the estimated parameters
v_predicted = model(x, params);

% Calculate the total sum of squares (SST)
sst = sum((v - mean(v)).^2);

% Calculate the residual sum of squares (SSE)
sse = sum((v - v_predicted).^2);

% Calculate r^2
rSquared = 1 - (sse / sst);
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main.m
disp(['a = ', num2str(a)]);
disp(['b = ', num2str(b)]);
disp(['r^2 = ', num2str(rSquared)]);
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