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Appendix B Main Menu 439
The Analyze Menu
Principal Components Derives a small number of independent linear combinations (principal
components) of a set of variables that capture as much of the variability in the original variables
as possible. JMP also offers several types of orthogonal and oblique Factor-Analysis-Style
rotations to help interpret the extracted components.
For details, see the JMP Statistics and Graphics Guide.
Discriminant Provides a method of predicting the level of a one-way classification based on
known values of the responses. The technique is based on how close a set of measurement
variables are to the multivariate means of the levels being predicted. Also, you can do stepwise
discriminate analysis. See the JMP Statistics and Graphics Guide for details.
PLS (Partial Least Squares) 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. See the JMP Statistics and Graphics Guide for details.
Item Analysis Enables you to estimate parameters for test items using Item Response Theory
(IRT). Also, choosing this enables you to access logistic 1PL, 2PL, and 3PL models.
B The Main Menu
Reliability and Survival
The Reliability and Survival submenu has a submenu with the following commands:
Life Distribution Lets you can find the most suitable distributional fit for your data and make
predictions. Weibull, Lognormal, Fréchet, Extreme Value, and other common distributions used
in Reliability and Survival analysis are included.
Fit Life by X helps you analyze lifetime events when only one factor is present.
Recurrence Analysis Looks at the age of a system when it requires a repair. A system can have
multiple repairs, each with its associated age, and is followed until it is no longer in service. A
typical system might be some component of an engine or appliance.
Survival Performs a univariate survival analysis using product-limit life table survival
computations with estimation of Weibull, lognormal, and exponential parameters.
Fit Parametric Survival Launches the Fit Model window, with the Parametric Survival fitting
personality in effect. The analysis tests the fit of an exponential, Weibull, or lognormal
distribution.
Fit Proportional Hazards Launches the Fit Model window, with the Proportional Hazards fitting
personality in effect. This regression analysis fits a Cox model.
Note: You can also use the Nonlinear platform to handle nonlinear models with loss functions for
other parametric survival modeling. See the JMP Statistics and Graphics Guide for details.