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    It's one of the most preferred options when it comes to furnishing children's rooms. The twin measurements are practically identical to the single.

    However, it is intended to be one of a pair of single beds. They are usually put in guest rooms which would be shared by more than just one person.

    They are also commonly chosen by couples who prefer to sleep together but not to share their bed as they don't feel comfortable. It's also important to note that there are a few different variations of the twin size bed and the most popular one of them is the Twin XL.

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    Weights for the fit, specified as the comma-separated pair consisting of 'Weights' and a vector the same size as the response data y curves or z surfaces.

    Values to assign to the problem-dependent constants, specified as the comma-separated pair consisting of 'problem' and a cell array with one element per problem dependent constant.

    For details, see fittype. Data Types: cell double. Smoothing parameter, specified as the comma-separated pair consisting of 'SmoothingParam' and a scalar value between 0 and 1.

    The default value depends on the data set. Only available if the fit type is smoothingspline. Proportion of data points to use in local regressions, specified as the comma-separated pair consisting of 'Span' and a scalar value between 0 and 1.

    Only available if the fit type is lowess or loess. Robust linear least-squares fitting method, specified as the comma-separated pair consisting of 'Robust' and one of these values:.

    Lower bounds on the coefficients to be fitted, specified as the comma-separated pair consisting of 'Lower' and a vector.

    The default value is an empty vector, indicating that the fit is unconstrained by lower bounds. If bounds are specified, the vector length must equal the number of coefficients.

    Find the order of the entries for coefficients in the vector value by using the coeffnames function. Individual unconstrained lower bounds can be specified by -Inf.

    Upper bounds on the coefficients to be fitted, specified as the comma-separated pair consisting of 'Upper' and a vector. The default value is an empty vector, indicating that the fit is unconstrained by upper bounds.

    Initial values for the coefficients, specified as the comma-separated pair consisting of 'StartPoint' and a vector.

    If no start points the default value of an empty vector are passed to the fit function, starting points for some library models are determined heuristically.

    For rational and Weibull models, and all custom nonlinear models, the toolbox selects default initial values for coefficients uniformly at random from the interval 0,1.

    As a result, multiple fits using the same data and model might lead to different fitted coefficients. To avoid this, specify initial values for coefficients with a fitoptions object or a vector value for the StartPoint value.

    Available when the Method is NonlinearLeastSquares. Algorithm to use for the fitting procedure, specified as the comma-separated pair consisting of 'Algorithm' and either 'Levenberg-Marquardt' or 'Trust-Region'.

    Maximum change in coefficients for finite difference gradients, specified as the comma-separated pair consisting of 'DiffMaxChange' and a scalar.

    Minimum change in coefficients for finite difference gradients, specified as the comma-separated pair consisting of 'DiffMinChange' and a scalar.

    Display option in the command window, specified as the comma-separated pair consisting of 'Display' and one of these options:.

    Maximum number of evaluations of the model allowed, specified as the comma-separated pair consisting of 'MaxFunEvals' and a scalar.

    Maximum number of iterations allowed for the fit, specified as the comma-separated pair consisting of 'MaxIter' and a scalar.

    Termination tolerance on the model value, specified as the comma-separated pair consisting of 'TolFun' and a scalar. Termination tolerance on the coefficient values, specified as the comma-separated pair consisting of 'TolX' and a scalar.

    Fit result, returned as a cfit for curves or sfit for surfaces object. See Fit Postprocessing for functions for plotting, evaluating, calculating confidence intervals, integrating, differentiating, or modifying your fit object.

    Goodness-of-fit statistics, returned as the gof structure including the fields in this table. Degree-of-freedom adjusted coefficient of determination.

    Fitting algorithm information, returned as the output structure containing information associated with the fitting algorithm.

    Fields depend on the algorithm. For example, the output structure for nonlinear least-squares algorithms includes the fields shown in this table.

    Number of unknown parameters coefficients to fit. Describes the exit condition of the algorithm. Positive flags indicate convergence, within tolerances.

    Zero flags indicate that the maximum number of function evaluations or iterations was exceeded. Negative flags indicate that the algorithm did not converge to a solution.

    There are also programs specifically written to do curve fitting; they can be found in the lists of statistical and numerical analysis programs as well as in Category:Regression and curve fitting software.

    From Wikipedia, the free encyclopedia. For placing "fitting" variable-sized objects in storage, see Fragmentation computer.

    Main article: Polynomial regression. See also: Polynomial interpolation. Further information: Computer representation of surfaces. CRC Press, Curve Fitting for Programmable Calculators.

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