Plot Linear Regression Matlab

Linear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is often used in data analysis and machine learning applications to make predictions or to identify patterns in data. In this article, we will discuss how to plot linear regression in Matlab.

Matlab is a powerful software tool used in various scientific and engineering fields. It offers a range of built-in functions and tools for data analysis and visualization. One of the most commonly used functions in Matlab for linear regression is the "regress" function.
The "regress" function in Matlab takes two arguments, X and Y, where X is a matrix of predictor variables and Y is a vector of response variables. The function returns a vector of regression coefficients that can be used to predict the response variable based on the predictor variables.

To plot the linear regression line in Matlab, we can follow the following steps:

Step 1: Load the data

First, we need to load the data that we want to analyze. We can use the "load" function in Matlab to load a dataset from a file.

Step 2: Perform linear regression analysis

Once we have the data, we can use the "regress" function in Matlab to perform linear regression analysis. The function returns a vector of regression coefficients, which we can use to predict the response variable.

Step 3: Plot the data and regression line

To plot the data and regression line, we can use the "plot" function in Matlab. We can first plot the data points using the "scatter" function, and then plot the regression line using the "plot" function.

Here is an example of how to plot linear regression in Matlab:

                        % Load the data
                        data = load('data.txt');

                        % Extract the predictor variable and response variable
                        X = data(:,1);
                        Y = data(:,2);

                        % Perform linear regression analysis
                        B = regress(Y, [ones(size(X)), X]);

                        % Plot the data and regression line
                        scatter(X, Y);
                        hold on;
                        plot(X, B(1) + B(2)*X, 'r');
                        hold off;
                    

In this example, we load the data from a file called "data.txt". We then extract the predictor variable and response variable from the data. We use the "regress" function to perform linear regression analysis and obtain the regression coefficients. Finally, we use the "scatter" function to plot the data points and the "plot" function to plot the regression line. The "hold on" and "hold off" commands are used to prevent Matlab from clearing the plot when we plot the regression line.