Modeling and Multivariate Methods - SAS

48 Introduction to the Fit Model Platform Chapter 2
Fitting Personalities
Transformations
The Transform options transform selected continuous effects or Y columns.
The Arrhenius transformation isT * = ------------------------------------
11605
Temp + 273.15
Fitting Personalities
The Fit Model platform provides many different fitting methods, called personalities in JMP. Normally, the
personality is automatically selected from the context of the response and the factors that you enter, but you
can change the personality from the Personality menu.
Table 2.5 briefly describes each personality and provides references to further details.
Table 2.5 Description of Fitting Personalities
Personality Description Reference
Standard Least Squares
Stepwise
Manova
LogLinear Variance
Nominal Logistic
Ordinal Logistic
Provides a continuous-response fit to
a linear model of factors using least
squares.
Stepwise regression is an approach to
selecting a subset of effects for a
regression model. The Stepwise
feature computes estimates that are
the same as those of other least
squares platforms, but it facilitates
searching and selecting among many
models.
Fits multiple Y variables to the set of
specified effects and provides
multivariate tests.
Models the variability in the
response.
Fits a logistic regression to a nominal
response.
Fits a logistic regression to an ordinal
response.
See the “Fitting Standard Least
Squares Models” chapter on
page 53.
See the “Fitting Stepwise
Regression Models” chapter on
page 133.
See the “Fitting Multiple Response
Models” chapter on page 155.
See the “Fitting Dispersion Effects
with the Loglinear Variance
Model” chapter on page 303.
See the “Performing Logistic
Regression on Nominal and
Ordinal Responses” chapter on
page 197.
See the “Performing Logistic
Regression on Nominal and
Ordinal Responses” chapter on
page 197.