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406 JMP Starter Appendix A
The Reliability Category
Discriminant Clicking the Discriminant button, or choosing Analyze > Multivariate Methods >
Discriminant, starts a discriminant analysis. The technique is based on how close a set of
measurement variables are to the multivariate means of the levels being predicted. Also, you
could do a stepwise discriminant analysis.
PLS Clicking the PLS button, or choosing Analyze > Multivariate Methods > PLS, fits models
using the partial least squares (PLS) method that balances the two objectives of explaining
response variation and explaining predictor variation. The PLS techniques work by extracting
successive linear combinations of the predictors, called factors (also called components or latent
vectors) that address one or both of these two goals. The PLS platform in JMP also enables you to
select the number of extracted factors by cross validation, which involves fitting the model to part
of the data and minimizing the prediction error for the unfitted part.
Item Analysis Clicking the Item Analysis button, or choosing Analyze > Multivariate Methods
> Item Analysis, provides analysis of test items using the Item Response Theory.
The Reliability Category
Reliability data contain duration times until the occurrence of a specific event and are sometimes
referred to as event-time or survival data. In survival data, the event can be failure, such as the failure of
an engine or death of a patient.
Figure A.6 JMP Starter Reliability
The buttons on the Reliability category, also found under Analyze > Reliability and Survival, can help
you analyze survival data several ways:
Life Distribution Is used to find the most suited distributional fir for your data, and to make
predictions. Weibull, Lognormal, Frechet, Extreme Value, and other common distributions are
included.
Fit Life By X Is used to analyze lifetime events when only one factor is present. Transformations
include Arrhenius, Erying, voltage, and linear. This platform also enables you to create a custom
transformation of your data. You can also compare different distributions at the same factor level