Basic Analysis • Distributions • Fit Distributions • Continuous Fit
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Continuous Fit
Use the Continuous Fit options to fit the following distributions to a continuous variable.
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The Normal distribution is often used to model measures that are symmetric with most of the values falling in the middle of the curve. JMP uses the unbiased estimate when determining the parameters for the Normal distribution.
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The LogNormal distribution is often used to model values that are constrained by zero but have a few very large values. The LogNormal distribution can be obtained by exponentiating the Normal distribution. JMP uses the maximum likelihood estimation when determining the parameters for the LogNormal distribution.
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The Weibull distribution, Weibull with threshold distribution, and Extreme Value distribution often provide a good model for estimating the length of life, especially for mechanical devices and in biology.
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The Exponential distribution is especially useful for describing events that randomly occur over time, such as survival data. The exponential distribution might also be useful for modeling elapsed time between the occurrence of non-overlapping events, such as the time between a user’s computer query and response of the server, the arrival of customers at a service desk, or calls coming in at a switchboard.
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The Gamma distribution is bound by zero and has a flexible shape.
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The Beta distribution is useful for modeling the behavior of random variables that are constrained to fall in the interval 0,1. For example, proportions always fall between 0 and 1.
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The Normal Mixtures distribution fits a mixture of normal distributions. This flexible distribution is capable of fitting multi-modal data. You can also fit two or more distributions by selecting the Normal 2 Mixture, Normal 3 Mixture, or Other options.
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The Smooth Curve distribution fits a smooth curve using nonparametric density estimation (kernel density estimation). The smooth curve is overlaid on the histogram and a slider appears beneath the plot. Control the amount of smoothing by changing the kernel standard deviation with the slider. The initial Kernel Std estimate is calculated from the standard deviation of the data.
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The Johnson Su, Johnson Sb, and Johnson Sl Distributions are useful for its data-fitting capabilities because it supports every possible combination of skewness and kurtosis.
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The Generalized Log (Glog) distribution is useful for fitting data that are rarely normally distributed and often have non-constant variance, like biological assay data.
Comparing All Distributions
The All option fits all applicable continuous distributions to a variable. The Compare Distributions report contains statistics about each fitted distribution. Use the check boxes to show or hide a fit report and overlay curve for the selected distribution. By default, the best fit distribution is selected.
The Show Distribution list is sorted by AICc in ascending order.
If your variable contains negative values, the Show Distribution list does not include those distributions that require data with positive values. Only continuous distributions are fitted by this command. Distributions with threshold parameters, like Beta and Johnson Sb, are not included in the list of possible distributions.
Related Information 
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Continuous Fit Distributions
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Fitted Quantiles
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Fit Distribution Options

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Help created on 9/19/2017