Analysis Types

# Histogram – Types, Examples and Making Guide

## Histogram

Definition:

Histogram is a graphical representation of the distribution of numerical data. It consists of a series of bars, where the height of each bar represents the frequency or relative frequency of the data within a particular interval or “bin.” The intervals, or bins, are typically specified on the x-axis, while the frequency or relative frequency is displayed on the y-axis. Histograms are commonly used in data analysis to visualize the distribution of a dataset, including information about its central tendency, spread, and skewness. They are particularly useful for identifying patterns and outliers in large datasets.

### Parts of Histogram

The parts of a histogram include:

#### X-axis

The x-axis, or horizontal axis, represents the range of values for the variable being measured. It is divided into intervals or bins, each of which represents a range of values.

#### Y-axis

The y-axis, or vertical axis, represents the frequency or relative frequency of the data within each interval or bin. The height of each bar represents the frequency or relative frequency of the data within that interval.

#### Bars

The bars in a histogram are vertical rectangles that represent the frequency or relative frequency of the data within each interval or bin. The width of the bars corresponds to the width of the interval or bin.

#### Intervals or bins

The intervals or bins represent the ranges of values that are grouped together in a histogram. They are typically of equal width and are specified on the x-axis.

#### Title

The title of the histogram should provide a clear and concise description of the variable being measured and the purpose of the histogram.

#### Legend

If the histogram represents multiple groups or categories, a legend may be included to explain the meaning of each color or pattern used to represent the data.

#### Skewness

The shape of the histogram can indicate whether the data is skewed to the left, right, or has a symmetrical distribution. This can be helpful in understanding the distribution of the data and making inferences about the population being measured.

### Types of Histogram

Some common types of Histogram are as follows:

#### Probability Histogram

A probability histogram is a type of histogram that shows the probability density function of a continuous variable. The area under each bar in a probability histogram represents the probability of the data falling within that range.

#### Bimodal Histogram

A bimodal histogram is a type of histogram that shows two distinct peaks, indicating that the data has two modes or two different populations. Bimodal histograms can indicate that the data is a mixture of two different distributions or that there are two underlying processes contributing to the data.

#### Uniform Histogram

A uniform histogram is a type of histogram that shows that the data is evenly distributed over a given range. In a uniform histogram, all bars are approximately the same height, indicating that there is an equal probability of the data falling within any given range.

#### Symmetric Histogram

A symmetric histogram is a type of histogram that shows that the data is evenly distributed around a central value, resulting in a shape that is roughly symmetrical. In a symmetric histogram, the mean, median, and mode are all approximately equal. This means that the distribution of the data is balanced, with roughly the same number of values on either side of the central value. An example of a symmetric histogram is the normal distribution.

### How to Make Histogram

Here are the general steps to make a histogram:

• Collect and organize the data: Collect the data you want to represent in the histogram. Group the data into intervals or bins, depending on the range and distribution of the data.
• Determine the range and interval width: Determine the minimum and maximum values of the data, and decide on an appropriate interval width or bin size. The bin size should be small enough to capture the variability in the data, but large enough to group similar values together.
• Draw the horizontal and vertical axes: Draw the horizontal axis and label it with the variable being measured. Draw the vertical axis and label it with the frequency or relative frequency of the data.
• Draw the bars: Draw rectangles above each interval or bin, with the height of the rectangle corresponding to the frequency or relative frequency of the data in that bin. The width of the rectangle should be equal to the bin size.
• Add titles and labels: Add a title to the histogram that describes the variable being measured and the range of the data. Label the x-axis and y-axis with appropriate units and titles.
• Fine-tune the histogram: Adjust the histogram as needed to improve its readability and visual appeal. This may include changing the bin size, adjusting the scale of the axes, or changing the colors and styles of the bars.
• Interpret the histogram: Analyze the shape, center, and spread of the data using the histogram. Look for patterns and trends, and draw conclusions based on the data.

### Histogram Creating Guide

Here are the steps to create a histogram using a spreadsheet program like Microsoft Excel:

• Open a new spreadsheet and enter the data you want to use for the histogram.
• Create a column for the bins or intervals you want to use for the histogram. These bins should be evenly spaced and cover the entire range of your data.
• Select the data and the bin column, and then click on the “Insert” tab and select “Histogram” from the “Charts” section.
• Choose the desired histogram style and format for your chart. You can customize the colors, titles, axis labels, and other chart elements to suit your needs.
• Review the histogram and make any necessary adjustments. You may need to adjust the bin size, scale of the axis, or formatting of the bars to make the histogram more informative and visually appealing.
• Analyze the histogram and draw conclusions based on the data. Look for patterns, trends, and outliers in the data, and use the histogram to support your analysis and decision-making.

### Examples of Histogram

Here are some examples of histograms:

• Height of Students in a Class: A histogram of the height of students in a class might show a normal distribution with a peak around the average height of the class.
• Daily Temperatures in a City: A histogram of daily temperatures in a city might show a bimodal distribution, with one peak around the average high temperature and another peak around the average low temperature.
• Ages of Employees in a Company: A histogram of the ages of employees in a company might show a slightly skewed distribution, with more employees in their 30s and 40s than in their 20s or 50s.
• Grades on a Test: A histogram of grades on a test might show a uniform distribution if all the students performed equally well, or a skewed distribution if there are a few high or low scores that are significantly different from the others.
• Housing Prices in a Neighborhood: A histogram of housing prices in a neighborhood might show a skewed distribution with a long tail on the high end, indicating that there are a few very expensive houses in the area.

### Applications of Histogram

Histograms have many applications in various fields, including:

• Quality Control: Histograms are used in quality control to monitor the distribution of product characteristics, such as weight, dimensions, or color. By analyzing histograms, manufacturers can identify and correct problems with production processes and ensure that products meet quality standards.
• Market Research: Histograms are used in market research to analyze data on consumer preferences, behavior, and demographics. By analyzing histograms, marketers can identify trends and patterns in consumer data and use this information to develop targeted marketing strategies.
• Finance and Economics: Histograms are used in finance and economics to analyze data on stock prices, interest rates, and other financial variables. By analyzing histograms, analysts can identify trends and patterns in financial data and use this information to make investment decisions and develop economic models.
• Medical Research: Histograms are used in medical research to analyze data on patient characteristics, such as age, weight, and medical history. By analyzing histograms, researchers can identify risk factors for disease, track the progress of treatment, and identify patterns in health outcomes.
• Image Processing: Histograms are used in image processing to analyze and manipulate digital images. By analyzing histograms of image pixels, software can adjust image contrast, brightness, and color balance to enhance image quality and improve visual clarity.

### When to use Histogram

They are particularly useful for:

• Identifying the shape of a distribution: Histograms can help you identify the shape of a distribution, including whether it is symmetric, skewed, or bimodal.
• Identifying central tendency: Histograms can help you identify the center of a distribution, including the mean, median, and mode.
• Identifying variability: Histograms can help you identify the range and spread of a distribution, including the minimum and maximum values, as well as the interquartile range and standard deviation.
• Identifying outliers: Histograms can help you identify outliers, or extreme values that are significantly different from the rest of the data.
• Comparing distributions: Histograms can help you compare the distributions of two or more variables to identify similarities and differences.

### Purpose of Histogram

The purpose of a histogram is to visualize the distribution of a dataset. It provides a graphical representation of the frequency or proportion of data points that fall within each interval or bin of a continuous variable. Histograms can reveal patterns and trends in the data that may not be apparent from other methods of analysis, and can help you identify the shape, center, spread, and outliers of a distribution.

Histograms are particularly useful for identifying the following:

• The shape of a distribution: Histograms can help you identify whether a distribution is symmetric, skewed, bimodal, or uniform.
• The center of a distribution: Histograms can help you identify the mean, median, or mode of a distribution.
• The spread of a distribution: Histograms can help you identify the range, interquartile range, or standard deviation of a distribution.
• Outliers: Histograms can help you identify values that fall far outside the bulk of the distribution, which may be unusual or extreme.

Here are some advantages of using a histogram to analyze data:

• Easy to interpret: Histograms provide a visual representation of the data that is easy to understand and interpret. The bars in a histogram show the frequency or proportion of data points that fall within each interval or bin, making it easy to see the distribution of the data.
• Reveals patterns and trends: Histograms can reveal patterns and trends in the data that may not be apparent from other methods of analysis. By looking at the shape of the distribution, you can identify whether it is symmetric, skewed, bimodal, or uniform, which can provide insights into the underlying data.
• Identifies outliers: Histograms can help you identify outliers, or data points that fall far outside the bulk of the distribution. This can be useful for identifying unusual or extreme values that may require further investigation.
• Quantitative analysis: Histograms provide a quantitative analysis of the data that can be used to calculate measures such as the mean, median, mode, range, interquartile range, and standard deviation. This can help you gain a more precise understanding of the distribution of the data.
• Comparisons: Histograms can be used to compare the distribution of two or more variables, which can reveal similarities and differences in the data.

### Limitation of Histogram

• Bin size: The shape of the histogram can be affected by the bin size or width, and choosing the appropriate bin size can be subjective. A small bin size can lead to a jagged or noisy histogram, while a large bin size can oversimplify the distribution and obscure important features.
• Outliers: Histograms can be affected by outliers, which are data points that fall far outside the bulk of the distribution. Outliers can skew the distribution and make it difficult to interpret the data.
• Noisy data: Histograms can be sensitive to noisy or incomplete data, which can affect the shape and interpretation of the distribution.
• Subjectivity: The interpretation of histograms can be subjective, and different analysts may choose different bin sizes or interpret the distribution differently.
• Limited to one variable: Histograms are limited to analyzing one variable at a time, which can make it difficult to identify relationships between variables. 