Several different types of graphs may be useful for analyzing data. These include stem-and-leaf plots, scatter plots, box plots, histograms, quantile-quantile (QQ) plots, and autocorrelation plots.
A stem-and-leaf plot consists of a “stem” that reflects the categories in a data set and a “leaf” that shows each individual value in the data set.
A scatter plot consists of a series of points that reflect observations from two data sets. The plot shows the relationship between the two data sets.
A box plot shows summary measures for a data set. The plot takes the form of a rectangle whose shape represents measures such as the minimum value, the maximum value, the quartiles, and so on.
A histogram shows the distribution of a data set as a series of vertical bars. Each bar represents a category (usually a numerical value or a range of numerical values) found in a data set. The height of each bar represents the frequency of values in the category. Histograms are often used to identify the distribution a data set follows.
A QQ (quantile-quantile plot) compares the distribution of a data set with an assumed distribution.
An autocorrelation plot is used to show how closely related the elements of a time series are to their own past values.
