Histogram Histogram
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A histogram is a collection of rectangles with bases and the intervals between class borders. Each rectangular bar represents some kind of data, and they are all adjacent. Rectangle heights are proportional to matching frequencies of similar and distinct classes.It was introduced by Karl Pearson.

This useful data collection and analysis tool is one of the seven fundamental quality tools.

Exploratory Research Guide

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What Is Histogram?

A histogram is a graphical representation of data that groups data into continuous numerical ranges, with each range represented by a vertical bar. The number ranges are determined by the data being used. Histogram appears to be a bar chart, however there are significant distinctions between them as histogram are used with continuous data to illustrate the frequency of variable occurrences Bins are used to categorize the continuous data. These bins make it simple to identify the majority and minority positions. Also, while designing a histogram, it isensure that the bins are not too narrow, which might disrupt the frequency distribution flow, or too thick, which makes it difficult to observe the change in data.

  • The number range is shown on the horizontal axis.
  • The amount of data in each range is represented by the vertical axis (frequency).

Parts Of A Histogram

The Title: The title of a histogram is the most significant section. The title of the histogram informs us what it is about. In other words, it explains the data presented by the histogram.

The X-axis: It is an interval that specifies the value scale under which the measurements fall.

The Y-axis: displays the probability of values that occur inside the intervals defined by the X-axis.

The Bars: Finally, there are the bars, whose height denotes the number of times the values happened inside the period and whose width represents the range that is covered.

Properties Of A Histogram

Before attempting to construct a histogram or knowing how to generate a histogram, it is critical to understand the general guidelines. Though they are not specified anywhere, looking over the ones designed with years of expertise with histograms would help you understand them better.

A number of classes or bins: Bins, which are the ranges on the X-axis of the histogram, are often referred to as classes, and each class has the same data distribution. A histogram can have as many bins as needed, but there must be a minimum and maximum value. If these are not taken into account beforehand, the graphical depiction loses its value.

Bin width: Now that we know the highest and minimum values of our histogram, we must understand how to disperse them in order to keep the data readable.

This means that every range, bin, or class in a histogram should be the same. The distribution of the numbers between the maximum and minimum should be equal in order to fulfil the aim of the graphical representation with equal weight-age.

For example, in a monthly salary histogram, the least income may be INR 5000 and the maximum compensation could be INR 40,000. To distribute them evenly, we can divide them into groups such as INR 5000-10,000, INR 10,001-15,000, and so on up to INR 40,000.

How Histograms Work

Histograms are often used in statistical data to show how many of a particular type of variable occurring within a given range. A census focused on a country’s demographics, for example, may use a histogram to illustrate how many individuals are between the ages of 0 – 10, 11 – 20, 21 – 30, 31 – 40, 41 – 50, and so on.

The analyst can modify histograms in a variety of ways. The first step is to adjust the distance between buckets.

Another thing to think about is how to determine the y-axis. The simplest basic label is the frequency of events detected in the data, however proportion of total or concentration might also be used instead.

The ideal tool is the histogram since the findings of the survey can be easily extracted, whether it is the census, mortality rate, or life expectancy rate, for example.

Applications Of Histogram

Set goals or objectives: After creating the histogram, you may opt to lower the mean and extreme variance in the process, bringing it back into compliance with existing or new criteria.

Demonstrate process capabilities: If the customer’s specifications are known, they can be plotted on the histogram to demonstrate how much the product, service, or test results fall short of expectations.

Data should be stratified: When the data considered to be producing variance is stratified (what, when, where, and who), the key drivers of the difference become more evident.

Confirm the conclusions: A change in the data distribution might show efficacy in targeting the fundamental causes of the problem by comparing histograms before and after remedies have been adopted.

Compare the outcome: Histograms can provide the knowledge we need to identify the key problem by comparing productivity rates of two operators operating the same machine on separate shifts, or two physicians with different patient discharge rates, or equipment dependability of two distinct maintenance teams.

Importance Of Histogram

Creating a histogram allows you to see how data is distributed visually. Histograms may show a great quantity of data as well as the frequency. The frequency distribution will be calculated and returned by the function. It may be used to determine the frequency of values in a dataset.Because it is underused, the Histogram is often referred to as the “Unsung Hero of Problem-Solving.” For example, if you were attempting to solve a staff retention issue, you may generalize terminations by job classification and discover that 30% of those who departed last year were technicians. However, if we first applied a Histogram to the same population of terminated employees, but this time by how long they had been employed before termination, the histogram might reveal that 70% of them quit in less than six months, and half of them were nurses, despite the fact that overall nurse turnover was only 25% for the year.The Histogram indicated when they were departing throughout the retention process, which in this situation may shift our attention to hiring, onboarding, and mentoring rather than salary or recognition policies, for example.

How To Plot A Histogram?

  1. Begin by labelling the X-axis with class intervals and the Y-axis with frequencies.
  2. The scales on both axes must be the same.
  3. Class intervals must be mutually exclusive.
  4. Draw triangles with bases representing class intervals and heights representing related frequencies.
  5. Because the class boundaries are specified on the horizontal axis and the frequencies are shown on the vertical axis, a rectangle is constructed on each class interval.
  6. If the intervals are identical, the height of each rectangle is proportional to the associated class frequency.
  7.  If the intervals are uneven, the area of each individual rectangle is proportional to the associated class frequency.


Jeff works as a branch manager at a small bank. Jeff has recently received consumer feedback indicating that the wait times for a client to be handled by a customer care professional are excessive. Jeff chooses to watch and record the amount of time each client spends waiting. Here are his conclusions after monitoring and documenting the wait times of 20 customers:

So, Jeff can conclude that the majority of customers who wait is between 35.1 and 50 seconds.

Histgram Analysis

Step-I Before making any inferences from the histogram, ensure that the process was running regularly throughout the time period under consideration. If any exceptional occurrences occurred during the time period of the histogram, the study of the histogram form is unlikely to be generalizable to all time periods.

Step -II Examine the significance of the shape of your histogram.

Typical Histogram Shapes


The bell-shaped curve known as the “normal distribution” is a typical pattern. In a normal or “typical” distribution, points are equally likely to appear on either side of the average. Because many continuous data in nature and psychology exhibit this bell-shaped curve when assembled and graphed, the normal distribution is the most important probability distribution in statistics.


Data is deemed skewed in a statistical distribution when the curve seems curved or skewed to the left or right. The graph depicts symmetry in a normal distribution, suggesting that there are equal numbers of data values on the left and right sides of the median.

There are two types of skewed distribution:

  • Positive skewed (or right-skewed)
  • Negative skewed (or left-skewed)


The bimodal distribution is reminiscent of the back of a two-humped camel. The results of two processes with varying distributions are merged into a single set of data. A distribution of production data from a two-shift operation, for example, might be bimodal if each shift yields a separate distribution of outcomes. This issue is frequently shown via stratification.


The plateau might be described as a “multimodal distribution.” Several normal-distribution processes are coupled. The top of the distribution resembles a plateau due to the close proximity of the peaks.

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The edge peak distribution is similar to the regular distribution except for a high peak at one tail. Typically, this is due to a defective histogram creation, with data grouped together into a category labelled “greater than.”


A comb distribution is so named because it resembles a comb, with alternating high and low peaks. Rounding off might result in a comb form. For example, if you measure the height of the water to the closest 10 cm and the class width for the histogram is 5 cm, you may get a comb form.


The truncated distribution resembles a normal distribution without the tails. The provider may produce a regular distribution of material and then rely on inspection to distinguish what is within specification limits from what is not. The heart cut is the resulting shipments to the client from inside the requirements.


The dog food distribution is lacking something—average outcomes. If a consumer obtains this type of distribution, someone else will receive a heart cut, leaving the customer with “dog food,” the odds and ends left over after the master’s supper. Even if the product received by the client is within requirements, it falls into two clusters: one near the higher specification limit and one near the lower specification limit. This variance frequently causes issues in the customer’s procedure.


A histogram with a uniform shape implies extremely consistent data; the frequency of each class is relatively similar to the others. A data set with a uniformly shaped histogram might be multimodal, meaning it has numerous intervals with the highest frequency. The data may not be separated into sufficiently discrete intervals or classes, which is an indicator of a uniform distribution. Another option is that the histogram’s scale has to be modified in order to provide useful information.

Pros And Cons Of Histogram

PROS OF HISTOGRAM: Although histograms are among of the most often used graphs to represent data, the histogram has several advantages and disadvantages concealed inside its formulaic structure. Histograms allow users to simply compare data and work well with a wide variety of information. They also give a more solid type of consistency because the intervals are always equal, allowing for simple data transfer from frequency tables to histograms. Despite the fact that it is helpful in a variety of situations, histograms are most beneficial when dealing with broad value ranges.

CONS OF HISTOGRAM: Even though there are numerous situations in which utilizing a histogram is advantageous, there are also many situations in which using or interpreting a histogram can be difficult. For instance, unless the histogram is a frequency histogram, it is exceedingly difficult and almost impossible to extract the precise quantity of “input” in the histogram.

For example, if you were given a histogram and asked how many people responded to a survey, determining an exact number would be incredibly difficult. Histograms are frequently seen as cumbersome when comparing many categories, because comparing several histograms side by side does not produce the intended result.

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