|2D bar chart|
|3D bar chart of matrix data|
|Statistical box plots|
|Histogram plots of data|
|2D pie charts|
|3D pie charts|
|Statistical quantile-quantile plots|
|Import of STL graphics files|
|Area of a histogram plot|
|Mode for computing quantile lines in box plots|
|Classes of histogram plots|
|Interpretation of the classes in histogram plots|
|The (statistical) data to plot|
|Density values for a density plot|
|Heights of pieces in pie charts|
|Size of a point list|
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],
..., [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
Bar charts, histograms, and pie charts help you compare different data samples, categorize data, and see the distribution of data values across a sample. These types of plots are very useful for communicating results of data analysis. Bar charts, histograms, and pie charts can help your audience understand your ideas, results, and conclusions quickly and clearly.
Box plots reduce data samples to a number of descriptive parameters.
Box plots are very useful for a quick overview and comparison of discrete
data samples. To create a box plot, use the
plot::Boxplot function. For example,
create a box plot for the data samples
contain random floating-point numbers from the interval [0.0, 1.0)
and the value 2 (the outlier):
Quantile-quantile plots help you determine whether two samples come from the same distribution family. Quantile-quantile plots are scatter plots of quantiles computed from each sample together with a reference line along the diagonal of the plot. If the data forms the line, it is reasonable to assume that the two samples come from the same distribution family. If the data falls near the reference line, you also can assume that the two samples have the same mean and the same variance.