Can I use lsqcurvefit for a multivariable nonlinear regression

I just wanted to ask whether the way I am using the function is correct and I can use lsqcurvefit for this type of multivariable nonlinear regression?

% Code: 
initialParams_eps_2 = [0.6, 2.9, 0.5, 0.00115, 0.8, 0.0]; % initial guess for coefficients 
% data taken from excel sheet:
X_2 = [p q pe_plus eps_ampl eta_av OCR];

% Model Function: 
ModelFunc_3 = @(b, x) (2./sqrt(6+(1/3).*((M^2*(2.*x(:,1)-x(:,3)))./(x(:,2))))).*((x(:,4)./eps_ampl_ref).^(b(1))).*(exp(b(2).*...
    (x(:,5)./M))).*(exp(-b(3).*(x(:,6)-1))).*(((b(4).*b(5))./(1+b(5).*N))+b(4).*b(6));

% Number of parameters
numParams = 6; 

% Lower bounds - setting 0 for parameters with no lower bound restrictions

% lb = -inf(1, numParams);  % Start with no lower bounds on any parameters
lb(1:6) = 1e-6;  % Ensuring all coefficients are positive values 

% Upper bounds - assuming no upper bounds are necessary
ub = inf(1, numParams);  % Infinite upper bounds (no upper bound restrictions)

options_2 = optimoptions('lsqcurvefit', 'Display', 'iter', 'Algorithm', 'trust-region-reflective');

[solution_eps_2b, resnorm, residual, exitflag, output] = lsqcurvefit(ModelFunc_3, initialParams_eps_2, X_2, eps_dev_rate, lb, ub, options_2);

Answer 1

[Matt J]

Yes, lsqcurvefit can be applied to problems of any dimension.