At its most basic, a histogram can be used to represent numeric values (volume or population) on a graph that shows how they are distributed across different ranges of values (percentages or categories). This allows you to quickly identify trends and outliers in your data without having to manually analyze all of the individual numbers.

A great example of this concept is using histograms to demonstrate population density in a particular region or country. By looking at a single graph, we can quickly identify which areas are more densely populated than others and therefore might require additional resources or support for infrastructure.

When charting or plotting data with histograms, it is important that you use the most appropriate visual elements so that your audience can clearly “see” any patterns or trends in your information. To do this, you need to consider things like:

- Labels for each variable displayed on the graph
- Variable binning or classes so that ranges of data can be grouped together for easier analysis and comparison
- An appropriate color scheme – either contrasting colors if possible – so that people can distinguish between different items on your graph with ease.

## What is a Histogram?

**A histogram** is a type of graph used to represent data. It is used to *visualize the distribution of a dataset* by showing the *frequency of the data*. It is a great way of understanding the data visually, as it provides an easy way to compare and identify patterns in the data.

In this article, we will walk through an example of a histogram to better understand how they work.

### Definition

**A histogram** is a graphical representation of data using bars of different heights. It displays information about the number and spread of variables in a dataset and can be used to get an overview of the data or to identify outliers. The **x-axis** represents the range of values, while the **y-axis** typically represents the frequency that those values occur in the dataset.

For example, if we were tallying up how many people have attended an event for each year, we would have a set of values that look something like this:

- 1999 – 5 people
- 2000 – 7 people
- 2001 – 3 people
- 2002 – 8 people

We would then graph this information by setting up a *vertical axis (y-axis)* representing frequency and a *horizontal axis (x-axis)* representing years. We could then draw bars that correspond with these numbers to form a histogram. The resulting image would look like this:

1999 |

### Types of Histograms

**Histograms** are a type of data visualization that illustrate the distribution and frequency of values in a dataset. These diagrams take the form of a bar graph—with the height of bars relative to the frequency of data points. The x-axis track values while the y-axis tracks frequency (or the numbers at which these values occur).

In addition to illustrating overall patterns, histograms may also be used to *compare data distributions, identify outliers or anomalies, and distinguish between different types or frequencies of data*. Histograms are often used by scientists or marketing professionals for statistical analysis—to make comparisons on audiences or consumer behavior.

There are two main types of histograms: **single-variable (univariate) and double variable (bivariate)** histograms. Univariate means examining one variable at a time, while bivariate means examining two variables together.

**Single Variable Histogram**: A single variable histogram (also known as an univariate one) is designed to illustrate*how many values there are for each category in your dataset*. This might include understanding what percentage of people have certain interests or participate in certain activities.**Double Variable Histogram**: A double variable histogram (also known as a bivariate one) helps to explain relationships between two variables simultaneously. For example, this might include visualizing*how age affects car ownership rate in a given area*.

## How to Create a Histogram

**A histogram** is a great way to *visualize data*. It allows you to quickly see the *distribution of values* across a dataset. It is also a powerful tool for *identifying patterns* within the data.

In this article, we’ll show you how to create a histogram and explain the different components of a histogram:

### Preparation

When creating a histogram, it is important to develop a plan for how the data will be visualized. Proper planning ensures that the data is collected in an organized and efficient manner and that the chart is set up correctly.

Before beginning, be sure to consider:

- What type of information you need to collect
- How it should be formatted
- How you want to display it
- Any special features you want your chart to contain

Once the framework for your chart has been created, you can begin to gather your data. Start by taking an inventory of all of the elements you need to include in your graph. Establish what categories to use and figure out where each piece of information will fit on the graph. Then compile your data into these categories so that everything looks orderly, clear, and concise when presented on a histogram.

After all this preparation has been completed, double check that all of your data fits within established categories before assembling the chart itself. This last step ensures accuracy in data representation and confirms that the histogram properly communicates its message *effectively* when complete.

### Creating the Histogram

When working with data sets, it can be helpful to construct a visualization of the information using a **histogram**. A histogram is a graphical representation of numerical data that expresses the number or frequency of a certain value. It is useful for modeling the probability density function of a given variable, as well as making comparisons between distributions created from different datasets.

To create a histogram, begin by organizing your data set into classes on an x-axis. To do this, determine how many classes you want to display, and then divide the length of your range of values into that number of equal class widths. For example, if you had 10 numbers ranging from 0-100, you might want three classes with each class having an upper most limit of 25 (0-25), 50 (26-50), and 75 (51-75).

After setting up your histogram’s respective classes, use vertical columns to represent how often each class appears in your dataset. The area beneath each column represents its corresponding frequency in relation to all other frequencies (these must sum up to equal 100%). For example: if 12 out of 20 occurs at 30%, 12 would be represented by three vertical boxes with each box having an area of 10%.

It is important that when constructing this visualization that all side lengths remain equal so as to not distort either the x or y axis. Once complete, review your work and use it to identify trends in frequency and distribution among separate datasets involving similar variables or those variations now represented on the same graph for comparison against one another.

## Examples of Histograms

**A histogram** is a type of chart that visually displays data using bars of different heights. This type of chart is useful for describing the distribution of a dataset, as it shows the frequency of data within a specific range of values. Using a histogram, you can quickly identify the *range of values in which most of the data lies*.

Let’s take a look at some examples of **histograms**:

### Example 1: Frequency Distribution

**A frequency distribution**, also known as the *histogram*, is a type of graph used to display frequencies or the number of occurrences in a given interval. Generally, it uses rectangles or bars to represent values which are equal to the frequency of occurrence for each measured interval.

Histograms are useful for quickly summarizing data that is distributed across multiple categories or groups. By looking at the histogram and focusing on a few simple features, individuals can quickly determine what we can learn from our data and how we should process it.

**Example 1: Good Temperatures during Winter**

In this example, we have temperatures collected from 15 different locations duringÂ wintertime plotted on a histogram. The peak (or mode) of this graph shows us that most locations had an average temperature around **35 degrees Fahrenheit (-1 degree Celsius)**. The outliers at either end show us that some places were much colder than others during these months.

### Example 2: Grouped Frequency Distribution

**A grouped frequency distribution** is a type of histogram that groups numbers into ranges, or bins. This can be used to show the number of people in a dataset with similar characteristics.

For example, you may want to show the distribution of test scores in a classroom. To do this, you would take the scores and divide them into different score range *“bins”*, plotting the number of people who received each score range on the x-axis. In this case, a histogram would look something like this:

- Range (x-axis):
**0-10, 11-20, 21-30**etc. - Frequency (y-axis):
**Number of students**reaching each grade range

As you can see in this example, *most students achieved a score between 11 and 20*.

### Example 3: Cumulative Frequency Distribution

**A cumulative frequency distribution** is another way to graphically represent data on the same type of histogram. Cumulative frequency distributions display the proportion of observations in a dataset that are equal to or *less than a given value*. Such histograms plot the cumulative sum of frequencies (often expressed as percentages) by vertical columns, with each bar representing the total of this sum up to certain levels. The vertical axis usually shows percentages, while the horizontal axis always represents *increasing classes*.

Cumulative frequency histograms are useful for showing overall trends and patterns in data. For example, when you are looking at a large set of scores from a mathematics test, you can easily identify how many students have achieved above an average level of excellence by looking at the cumulative frequency distribution plot; or compare different test performance using cumulative sums for different educational systems or groups over time.

## Conclusion

**Data visualization** is an invaluable tool for inspecting and understanding data. *Histograms, in particular,* are a powerful way to easily identify key attributes of a dataset. They allow us to quickly see the overall shape of a dataset, its distribution, guard us against trying to draw conclusions from inadequate data samples, and provide hints about what type of modeling approach may work best given the dataset’s attributes.

It’s important to remember that histograms yield conclusions over specific ranges of values and thus can be used as a tool for both further investigation and hypothesis testing. Used correctly, they can help unlock the secrets contained within our data and give us insight into the bigger picture—all while taking up very little space!