Accelerating the pace of engineering and science

Documentation Center

• Trial Software

Create Scatter and List Plots

Scatter plots can help you identify the relationship between two data samples. A scatter plot is a simple plot of one variable against another. For two discrete data samples x1, x2, ..., xn and y1, y2, ..., yn, a scatter plot is a collection of points with coordinates [x1, y1], [x2, y2], ..., [xn, yn]. To create a scatter plot in MuPAD®, use the plot::Scatterplot function. For example, create the scatter plot for the following data samples x and y:

```x := [0.25, 0.295, 0.473, 0.476, 0.512,
0.588, 0.629, 0.648, 0.722, 0.844]:
y := [0.00102, 0.271, 0.378, 0.478, 0.495,
0.663, 0.68, 0.778, 0.948, 0.975]:
plot(plot::Scatterplot(x, y))```

By default, the plot::Scatterplot function also displays a regression line. This line shows the linear dependency that best fits the two data samples. To hide the regression line, use the LinesVisible option:

`plot(plot::Scatterplot(x, y, LinesVisible = FALSE))`

Another plot that can help you identify the relationship between two discrete data samples is a list plot. List plots are convenient for plotting one data sample with equidistant x-values. They are also convenient for plotting combined data samples, such as [[x1, y1], [x2, y2], ..., [xn, yn]]. If you have two separate data samples, you can combine the data of these samples pairwise:

`xy := [[x[i], y[i]] \$ i = 1..10]:`

To create a list plot, use the plot::Listplot function:

`plot(plot::Listplot(xy), AxesTitles = ["x", "y"])`

By default, the plot::Listplot function connects adjacent points on the plot by straight lines. To hide these connections, use the LinesVisible option:

```plot(plot::Listplot(xy),
AxesTitles = ["x", "y"],
LinesVisible = FALSE)```