JMP 13 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Predictive and Specialized Modeling
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 12 Online Documentation
Basic Analysis
• Bootstrapping
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Bootstrapping
Approximate the Distribution of a Statistic through Resampling
Bootstrapping is a re-sampling method for approximating the sampling distribution of a statistic. The data is re-sampled with replacement and the statistic is computed. This process is repeated to produce a distribution of values for the statistic.
Bootstrapping is useful when estimating properties of a statistic (mean, standard error, and so on) and performing inference, in the following situations:
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The theoretical distribution of the statistic is complicated or unknown.
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Inference using parametric methods is not possible due to violations of assumptions.
JMP provides bootstrapping for statistical platforms that support Frequency columns in which the rows are assumed to be independent.
The Bootstrap option is on the right-click menu, separate from standard platform commands.
Example of the Distribution for Bootstrapping Results
Contents
Example of Bootstrapping
Perform a Bootstrap Analysis
Bootstrap Window Options
Stacked Results Table
Unstacked Results Table
Analysis of Bootstrap Results