Customize the histogram

Click the graph to select it, then click the plus sign beside the graph and select items to display on the graph. The following information describes some of the items on the Graph Elements menu.

Scale Type

You can select one of the following types for the y-scale.

Frequency
The height of each bar represents the number of observations that fall within the bin.
Percent
The height of each bar represents the percentage of the sample observations that fall within the bin. A histogram with a percentage scale is sometimes called a relative frequency histogram. A percent scale can be useful when comparing samples of different sizes.
Density
The area of each bar represents the proportion of the sample observations that fall within the bin (proportion = bar area = bin width × bar height).
Cumulative Frequency
The bar heights accumulate from left to right. The height of each bar is equal to the number of observations that fall within the bin and all previous bins. The height of the final bar is always equal to the total number of observations in the sample.
Cumulative Percent
The bar heights accumulate from left to right. The height of each bar is equal to the percentage of the sample observations that fall within its bin and all previous bins. The height of the final bar is always equal to 100%.

Binning

Bins are equally spaced intervals used to sort sample data for graphing. In Minitab, histograms and dotplots plot the number of values that are in each bin.

Choose the number of bins

The number of bins affects the appearance of a graph. If there too few bins, the graph will be unrefined and will not represent the data well. If there are too many bins, many of the bins will be unoccupied and the graph may have too much detail. For example, these histograms represent the same data with different numbers of bins.

4 bins
15 bins
50 bins
Choose where to display the tick labels

Bins can be defined by either their midpoints (center values) or their cutpoints (boundaries). The appearance of the graph changes if you change the bin definition method.

Midpoint
Cutpoint

Distribution Fit

Add a fitted distribution line to assess whether your data follow a specific theoretical distribution. For example, many statistical analyses assume that data follow a normal distribution. Data that fit the distribution well have bars that closely follow the fit line. Minitab also estimates the distribution parameters from your sample and conducts an Anderson-Darling test to test whether your data fit the theoretical distribution.

Choose a distribution

The following distributions are available.
Note

To fit a lognormal distribution, an exponential distribution, or a Weibull distribution, all data values must be greater than 0.

Normal
The normal distribution is the most common statistical distribution because approximate normality occurs naturally in many physical, biological, and social measurement situations. Many statistical analyses assume that the data come from approximately normally distributed populations. For more information, go to Normal distribution.
Lognormal
A random variable follows the lognormal distribution if the logarithm of the random variable is normally distributed. Use the lognormal distribution when random variables are greater than 0. The lognormal distribution is used for reliability analysis and in financial applications, such as modeling stock behavior. For more information, go to Lognormal distribution.
Exponential
Use the exponential distribution to model the time between events in a continuous Poisson process. Independent events are assumed to occur at a constant rate. For more information, go to Exponential distribution.
Weibull
The Weibull distribution is a versatile distribution that can be used to model a wide range of applications in engineering, medical research, quality control, finance, and climatology. For example, the distribution is frequently used with reliability analyses to model time-to-failure data. The Weibull distribution is also used to model skewed process data in capability analysis. For more information, go to Weibull distribution.
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