JMP 12 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
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 13 Online Documentation
JMP 13.1 Online Documentation
JMP 13.2 Online Documentation
Fitting Linear Models
• Statistical Details
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Statistical Details
Fitting Linear Models
This appendix discusses the different types of response models, their factors, their design coding, and parameterization. It also includes many other details of methods described in the main text.
The JMP system fits linear models to three different types of response models that are labeled continuous, ordinal, and nominal. Many details on the factor side are the same for the different response models, but JMP only supports graphics and marginal profiles on continuous responses—not on ordinal and nominal.
Different computer programs use different design-matrix codings, and thus parameterizations, to fit effects and construct hypothesis tests. JMP uses a different coding than the GLM procedure in the SAS system, although in most cases JMP and SAS GLM procedure produce the same results. The following sections describe the details of JMP coding and highlight those cases when it differs from that of the SAS GLM procedure, which is frequently cited as the industry standard.
Contents
The Response Models
Continuous Responses
Nominal Responses
Ordinal Responses
The Factor Models
Continuous Factors
Nominal Factors
Ordinal Factors
Frequencies
The Usual Assumptions
Assumed Model
Relative Significance
Multiple Inferences
Validity Assessment
Alternative Methods
Key Statistical Concepts
Uncertainty, a Unifying Concept
The Two Basic Fitting Machines
Likelihood, AICc, and BIC
Power Calculations
Computations for the LSN
Computations for the LSV
Computations for the Power
Computations for the Adjusted Power
Inverse Prediction with Confidence Limits