Summary

Provides a static view of the data collected from a process. A histogram displays the basic distribution of the data, including where the central location is, the amount of variation, and whether it is skewed, normal, or symmetric. You can add normal curves (or fit 13 other distributions) to verify whether data are reasonably normal.

Answers the questions:
  • What is the general shape and location of a sample of Y data?
  • Does the sample contain any unusual data points (outliers)?
  • Are the data reasonably normal?
When to Use Purpose
Mid-project The first rule in data analysis is to always look at a graph of the data before conducting any kind of statistical test. The histogram is a logical choice for any tests in which you are comparing a process output to a standard. Histograms also help you determine whether the data are reasonably normal, a common assumption in many statistical tests.
Mid-project Histograms are good tools for communicating the distribution of a process output at various points in a project to the project stakeholders.

Data

Numeric Y or X (continuous or discrete)

How-To

  1. Enter data for each Y-variable in separate columns to generate a separate histogram for each column of Y data. The data you use to create the histogram can be either continuous or discrete (numeric).
  2. You can also have up to two categorical, or By variables. If you have categorical variables, Minitab creates a histogram of the Y data for each combination of values of the categorical variables. You can place these graphs in panels in the same window or in separate windows.

Guidelines

  • Histograms for small quantities of data can be somewhat misleading. The number of bars (bins) in the histogram is a function of the sample size. Small samples do not contain many bars, and their bars increase in width. Data points close to the edge of a bar are sometimes combined into one bar. A larger sample would have more bars.
  • You should not make assessments of normality with histograms when you have small samples (n < 50) because the potential exists for combining as described above. If you have small samples, you probably should use a probability plot.
By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy