Online Histogram Maker

Histograms are graphical representations of frequency distributions for numerical or categorical data generated within specified intervals known as bins. The distribution is constructed by creating a frequency table consisting of the frequencies in each bin and plotting them against the intervals. These are especially useful for large observations. The different distribution types for a histogram include normal, bimodal, right-skewed, left-skewed, and random distribution.

How to use this tool

1. Place the experimental data into the box on the right. This can be done by directly copying from Excel or pasting values in comma-separated, tab-separated, or space-separated formats. If the data is being entered manually, only place one value per line. To construct a histogram along with its density curve, enter raw data as shown below:

Data Set 1

X₁

X₂

X₃

X₄

…

Users can graph up to three data sets on the same plot for comparison purposes. To add a new data set, press on the "+" tab above the data entry area. Data sets can be renamed by double clicking the tab.

2. Verify your data is accurate in the table that appears.

3. Press the "Generate histogram" button to display results.

Data Entry

Load Data

Save Data

Import from File

Process data

Additional Information

Histograms are frequency distribution plots for a set of continuous data that allow for inspection of underlying distribution, such as normal distribution, outliers, skewness, etc. represented by the population. The data is split into classes, called bins where each bin represents a period containing the number of occurrences in the data set. The area of the bar is indicative of the frequency of occurrences for each bin, which is the product of the height multiplied by the width of the bin. The histogram is then constructed by tabulating the frequencies in each bin and plotting them against the intervals. There is no formula to determine the ideal bin size, but one must make sure that the bins are neither too small nor too large, in which case the underlying pattern of frequency distribution becomes elusive. Histograms represent continuous data sets and hence, do not have “gaps” between the bars, although bars might be absent reflecting no frequencies.

A histogram displays numerical or categorical data. These are useful to not only convey a large amount of information faster in the form of charts, but also estimate a variable’s mean, standard deviation, skewness, and kurtosis, all of which describe the underlying distribution. Histograms can answer questions like determining whether the outputs of two or more processes are distributed normally or if they are different, whether a process has changed over time intervals and if so, how the shapes of the distributions may vary, and analyzing whether processes can meet specific requirements.

There are several types of distributions found within histograms. A normal distribution is symmetric with the mean being at the center and the likelihood of points occurring on either side of the average being equal. Similarly, a bimodal distribution has two peaks instead of one and the data is analyzed as separate normal distributions. There is the right skewed and left skewed distribution where a large number of data values occur on the right or left side, respectively. Lastly, a random distribution is one that lacks a pattern and usually exhibits multiple peaks, in which case the data should be analyzed separately.

Term	Definition
Mean	The average numerical value of a dataset, which can be found by taking the sum of all values in the dataset and dividing it by the total number of values.
Standard Deviation	A measure of the amount of dispersion or variation between a set of data and its mean.
Skewness	A measure of the asymmetry or distortion in the distribution of a dataset. In other words, it quantifies the extent to which the data points are skewed or shifted to one side of the distribution.
Kurtosis	A measure of a dataset distribution's shape within statistics, gauging the degree to which the tails, or outliers, of a probability distribution deviate from those of a normal distribution.
Lowest Score	The lowest numerical value within the entire dataset.
Highest Score	The highest numerical value within the entire dataset.
Distribution Range	The difference between the highest and lowest scores within the dataset.
Total Number of Scores	The total number of values or data points within the dataset.
Number of Distinct Scores	The total number of distinct values or data points within the dataset.
Lowest Class Value	The bottom end of the lowest class range.
Highest Class Value	The top end of the highest class range.
Number of Classes	The total number of classes/bins.
Class Range	The size of each class/bin range.

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References

This online tool may be cited as follows

MLA	"Quest Graph™ Online Histogram Maker." AAT Bioquest, Inc., 22 Dec. 2024, https://www.aatbio.com/tools/online-histogram-maker.
APA	AAT Bioquest, Inc. (2024, December 22). Quest Graph™ Online Histogram Maker. AAT Bioquest. https://www.aatbio.com/tools/online-histogram-maker.
BibTeX	EndNote	RefMan