Nonlinear Regression in MATLAB: Fitting Complex Models to Data

Line regression is a powerful technique for fitting complex models to data that does not follow a linear relationship. MATLAB provides powerful tools to perform nonlinear regression analysis, enabling you to find the best-fit parameters for a model and make predictions based on the fitted model. In this tutorial, we will walk through the process of performing nonlinear regression in MATLAB using datasets available in the MATLAB environment.

Data set selection

For this tutorial, we will use a built-in MATLAB dataset called "carbig". The "carbig" dataset contains information about various car models, such as attributes such as mileage, weight, and horsepower. This dataset will serve as a good example to demonstrate nonlinear regression.

Step 1: Load and explore the dataset

First, let's load the "carbig" dataset and examine its structure and content. Open MATLAB and enter the following command:

% Load the carbig dataset
load carbig; 

This will load the dataset in the MATLAB workspace. Take a moment to explore the variables and their descriptions. The loaded dataset contains the following structural elements:

           Model: [406×36 char]
          Origin: [406×7 char]
             MPG: [406×1 double]
       Cylinders: [406×1 double]
    Displacement: [406×1 double]
      Horsepower: [406×1 double]
          Weight: [406×1 double]
    Acceleration: [406×1 double]
      Model_Year: [406×1 double]
            cyl4: [406×5 char]
             org: [406×7 char]
            when: [406×5 char]
             Mfg: [406×13 char]
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