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Design-Expert v23.1
What’s New
Installation
Getting Started
Tutorials
Designs
Pre-Experiment
Analysis
Optimization
Post Analysis
Python Integration (Stat-Ease 360
®
only)
Advanced Topics
Logistic Regression
Candidate Point Creation
Complex Constraints
Convert Coded response surface model to Actual
Converting Mixture Models from Pseudo to Real
Convert a quadratic L_Pseudo mixture model to Real
Convert a quadratic U_Pseudo mixture model to Real
Covariates and Additional Explanatory Variables
Design Augmentation
Equation Entry
Exponential Notation
Extrapolating a Mixture Design
Extrapolating a Response Surface Design
Foldover
Fraction of Design Space Computations
Fraction of Paired Design Space computations
Interpreting Models
Mean Bias Correction
Multiple Linear Constraints
Optimality Criteria
Optimal Exchange Methods
Propagation of Error
Standardized and Normalized Factorial Effects
Start the Design From Existing Data
Restricted Maximum Likelihood (REML) vs Maximum Likelihood (ML) Analysis
Hints and FAQs
Technical Support
Statistical Support
Credits
Design-Expert
»
Advanced Topics
»
Logistic Regression
»
Fit Statistics
» Parameters (p)
Parameters (p)
The number of parameters in the model (model degrees of freedom plus intercept if any).
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