RightChain Histograms

Histograms are one of many plot types, supported by RightChain AI. Histograms display the frequency of observations to within a range of values. A range of values is called a "bin". The user gives each plot a name, which appears as a clickable tab, above the visual. Users may also include a description for each plot. The description appears as the tab's tooltip. The choice of data elements for the X and Y axes are via dropdown menus. Users may also choose from among many statistics to display, including minimums, maximums, means, medians, first quartiles, third quartiles, and correlations. 

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Histogram Fundamentals

A histogram is a type of bar graph used in statistics to represent the distribution of numerical data by showing the frequencies of data points within certain ranges of values, called bins. Unlike bar charts, which are used for categorical data, histograms are used exclusively for continuous data, where the bars are adjacent to each other, indicating the continuous nature of the data.

Here’s how histograms are structured and used:

1. Bins: These are defined intervals or ranges into which the data points are grouped. Each bin represents a range of values, and all bins usually have the same width in a single histogram. The choice of bin size and width can significantly affect the representation of data.

2. Axes: A histogram has two axes. The x-axis lists the bins (often just showing the range of values included in each bin). The y-axis represents the frequency count of data points within each bin, although it can also represent probability or density if the histogram is normalized.

3. Bars: Each bar in a histogram represents the frequency of data points within a particular bin. The height of the bar is proportional to the frequency of the data points that fall within that bin’s range.

4. Purpose: Histograms are used to give a rough sense of the density of the data. They are particularly useful for identifying different data characteristics, such as skewness, modality (uni-modal, bi-modal, multi-modal), or the presence of outliers.

5. Analysis: By examining the shape and spread of the histogram, statisticians can gain insights into the underlying distribution of the data set, check for normality, and make further decisions for analyses.