Design-Expert v25.0
- What’s New
- Installation
- Getting Started
- Tutorials
- Designs
- Pre-Experiment
- Analysis
- Optimization
- Post Analysis
- Python Integration (Stat-Ease 360® only)
- Gaussian Process Models (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
» Logistic Regression Report
» Model Chi-Squared and P-Value
Model Chi-Squared and P-Value
The logistic regression report lists the \(\chi^2\) and p-value for the model. This likelihood ratio test is also called the \(G\) test of goodness-of-fit or the \(G^2\) test.
References