The MATLAB software program from Mathworks is an incredibly handy mathematics and engineering tool capable of performing advanced calculations and simulations. One of its most useful functions is carrying out many different types of data analysis. A common type of analysis is calculating the best-fit slope from a group of data points. MATLAB's "polyfit" functions performs this job nicely by fitting a polynomial line to the data points using least squares calculations. If the user chooses the degree of the polynomial line to be 1, the result is the linear best-fit slope of the data.

## Step 1

Define a relationship between an independent and dependent variable. The data may come from experimental data or you may define the relationship directly. For example, experimental data may be a measure of magnitude versus time. In this case, list 't' may consist of the numbers [1 2 3 4 5] and list 'm' may contain the values [2 4 6 8 10].

## Step 2

Decide whether the desired polynomial curve to fit the data should be of degree 1 (linear), 2 (quadratic) or higher.

## Step 3

Use the polyfit function in the form "polyfit(independent variable, dependent variable, polynomial degree)". In our example, and desiring a linear slope, type "polyfit(t,m,1)" and MATLAB will output the following: 2.0000 -0.0000

## Step 4

Note that the '2' in the output of the previous step is the linear best-fit slope of the data provided.