Histograms Help Visualize and Summarize Variable Data

Posted by on Jan 2, 2022 in Uncategorized | 0 comments

A histogram is a bar graph that summarizes the frequency of information occurring over time. They visually display summarized data showing the information’s frequency, shape, and central tendency. The first action I take when dealing with variable or continuous data is constructing a histogram to see how the information looks. It helps me answer questions such as, “What does the distribution look like?” “Is it normal?” “Is it shifted from the target value?” And so on. Of course, you can easily construct a histogram using any statistical software, such as Minitab, QI Macros, or even Excel.

• Pictorially summarize large amounts of data
• Visually see process centering, spread, and shape
• Provide beneficial information for predicting the future performance of a process (if the process is stable)
• Observe a change in the process by comparing two histograms
• Observe patterns in the data
• Provides clues to reducing variation and causes of problems
• Observe the consistency of a quality characteristic
• Graphically show the relationship between the capability of the process and the engineering specifications

To construct a histogram:

1. Collect the data. Typically, 30 or more measurements are required.
2. Decide on the classes or intervals for grouping the data
3. Create a tally sheet using these classes
4. Complete the tally sheet and summarize the data
5. Draw the histogram so that the height of the rectangle represents the number in the tally for that class

An example of a histogram, with the lower and upper specification limits added, is shown below. Since the distribution uses the whole tolerance (USL – LSL), a helpful strategy to improve this process would be identifying the various variation categories contributing to excess variation and looking for ways to reduce the variation in each type. I typically start using the 6Ms, i.e., Man, Machine, Method, Material, Measurement, and Mother Nature. The goal would be to reduce the width of the distribution by continually removing variation. A famous statistician, Ellis Ott, is quoted as saying, “Always, always, plot your data!” I have found that this is an excellent habit to use.