Curve Fitting Using MATLAB Assignment HelpMATLAB boasts of a curve fitting toolbox which consists of graphical user interfaces and M-file functions built on its sophisticated computing environment. This toolbox allows users to do the following:
- Preprocessing of data including smoothing and sectioning
- Data fitting (Parametric fitting is usually done using a toolbox library or custom equation while Non-parametric fitting can be done using various interpolates or a smoothing spline)
- Least squares ( standard linear, non-linear, weighted, constrained) and robust fitting procedures
- Determining the “goodness of fit” using fit statistics
- The curve fitting tool in the form of a GUI environment
- MATLAB command line environment
What are the differences between the command line and curve fitting tool environments?
These two environment are functionally equivalent. However, it is unacceptable to mix the two when performing a particular curve fitting assignment. For example, you are not allowed to import into the curve fitting tool a fit you have generated at the command line. But a fit can be created in the curve fitting tool and the related M-file generated. After that, the fit can then be recreated from the command line and the M-file modified. It is for this reason that our experts recommend that you use the curve fitting tool for most of your tasks. Also, the curve fitting tool has enhanced data analysis and exploration tools.
Avail of our curve fitting using MATLAB homework help if you are not familiar with the procedure of fitting data
Data fitting is usually done at the fitting GUI. First, you need to open the fitting graphical user interface by opening the curve fitting tool and clicking on the button “Fitting”. You will notice that the fitting GUI consist of two parts:
- The Fit Editor
- Specifying the current data set, name of the fit, and the exclusion rule.
- Using a library or custom equation, an interpolant, or a smoothing spline to evaluate whether a variety fits to the current data set
- Overriding the default fit options
- Comparing fit results
- The Table of Fits
- Keeping track of the current session’s fits and their data sets
- Producing a detailed fit results summary
- Saving and deleting the results of the fit
- Examining the graphical fit results
For example, in the image above, the graphical fit results indicate that:
- The polynomial equations’ fits and residuals are all similar. This make it extremely difficult to choose the best one
- An overall poor fit is indicated by the single-term exponential equation’s fit and residuals
- Examining the numerical fit results
- The goodness of fit statistics – It shows if the curve fits the data
- The confidence intervals on the fitted coefficients – It is used to determine accuracy
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