Learning Library
This guide provides some ways to assess the fit of a normal distribution to a continuous variable. See options for fitting and assessing the fit of other non-normal distributions in the Fitting Distributions guide.
- From an open JMP® data table, select Analyze > Distribution.
- Select one or more continuous variables from Select Columns and click Y, Columns.
- Click OK to generate a histogram (Histogram Only was selected in this example).
Normal Quantile Plot
Click on the red triangle for the variable, and select Normal Quantile Plot.
If the data more or less follow a straight line (fat pen test), we can conclude that the data are reasonably approximated by a normal distribution. For this example, we would conclude the distribution is approximately normal.
Fitting a Normal Distribution
- Select Continuous Fit > Fit Normal from the lower red triangle for the variable.
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In the resulting output, click on the red triangle for Fitted Normal Distribution and select Goodness of Fit.
Two Goodness of Fit tests are performed to evaluate if the normal distribution is a good fit to the data (Shapiro-Wilk and Anderson Darling). Both have large p-values indicating that there is no statistical evidence suggesting the normal distribution is not a good fit to these data.
Visit Basic Analysis > Distributions > Options for Continuous Variables > Normal Quantile Plot and Basic Analysis > Distributions > Options for Continuous Variables > Fit Distributions in JMP Help to learn more.