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A pie chart (or a circle chart) is a circular statistical graphic which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area ) is proportional to the quantity it represents.
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century. Statistical graphics developed through attention to four problems: [3]
The most common technique, first appearing in the 1850s, is to start with a proportional circle sized according to some total amount, and turn it into a pie chart to visualize the relative composition of the total, such as the percentage of a total population belonging to various ethnic groups.
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Represents one categorical variable which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents. For example, as shown in the graph to the right, the proportion of English native speakers worldwide; Line ...
The maximum number of slices that can be displayed is 30. Currently the default colors used for slices 15 onwards are all the same as the default color of slice 14. If the specified values add to 100 and |other= is set, the calculated percentage for that slice can sometimes turn out to be very strange (e.g., "1.4210854715202E-14%" instead of "0%")
A frequency distribution shows a summarized grouping of data divided into mutually exclusive classes and the number of occurrences in a class. It is a way of showing unorganized data notably to show results of an election, income of people for a certain region, sales of a product within a certain period, student loan amounts of graduates, etc.
This type of univariate data can be classified even further into two subcategories: discrete and continuous. [2] A numerical univariate data is discrete if the set of all possible values is finite or countably infinite. Discrete univariate data are usually associated with counting (such as the number of books read by a person).