How to use polyfit command to find the linear approximation of the data

Measurment course

Answer 1

[Image Analyst]

See attached demo. Adapt as needed.

Answer 2

[Star Strider]

A linear approximation is a first-degree polynomial, so specify it as:

p = polyfit(x, y, 1);

The polynomial degree is the third argument to the polyfit (link) function.

[Alwaleed Alzadjali]

And if i have table of data how i can use that command

[Star Strider]

You will need to choose the vectors in your data that correspond to the independent, x, and dependent, y variables.

To fit them to your data, use the code I provided to get the coefficient vector, p, of the linear fit. To create a line to plot, use the p vector in polyval with your independent variable:

y_fit = polyval(p, x);

then:

plot(x, y, '+')
hold on
plot(x, y_fit, '-r')
hold off
grid

I am not certain if you are referring to ‘table’ as a matrix or as a table data type. That is an important distinction. See the documentation on Matrix Indexing (link) and table (link) for addressing, and a discussion of the different data types.

[Image Analyst]

Let's say your table is called t and you want the first column to be x and the second column to be y. So you'd do

x = t{:, 1}; % Use braces
y = t{:, 2};

If t were a regular numerical matrix, you'd use parentheses instead of braces:

x = t(:, 1); % Use parentheses.
y = t(:, 2);