Histogram: Difference between revisions
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File:Example_histogram.png|Histogram | |||
File:Travel_time_histogram_total_n_Stata.png|Travel Time Histogram Total | |||
File:Travel_time_histogram_total_1_Stata.png|Travel Time Histogram Total 1 | |||
File:Cumulative_vs_normal_histogram.svg|Cumulative vs Normal Histogram | |||
File:Untitled_document_(1).jpg|Histogram | |||
File:Gumbel_distribtion.png|Gumbel Distribution | |||
File:Daisy_histogram.jpg|Daisy Histogram | |||
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Latest revision as of 11:11, 18 February 2025
Histogram
A Histogram is a graphical representation of the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable. To construct a histogram, the first step is to "bin" the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent and are often (but not necessarily) of equal size.
Overview[edit]
A histogram is used to summarize discrete or continuous data. In other words, it provides a visual interpretation of numerical data by showing the number of data points that fall within a specified range of values (called "bins"). It is similar to a vertical bar graph. However, a histogram, unlike a bar graph, shows no gaps between the bars.
Applications[edit]
Histograms are widely used in statistics, business, and economics to show results of continuous data such as time, weight, distance, etc. They also help provide an overview of the data and its spread.
Construction[edit]
A histogram is constructed by placing the class intervals, which are mutually exclusive ranges of data, on the horizontal axis and the frequencies on the vertical axis. The class intervals are placed on the scale and the frequency of a class is represented by the height of the bar above the class interval.
Interpretation[edit]
The shape of a histogram can tell a lot about the distribution of data. For example, data that is tightly bunched with a high kurtosis value will have a distinct peak near the mean, while data that is more spread out will have a flatter peak.
See Also[edit]
References[edit]
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