Advanced Statistical Modeling
Advanced techniques that don't require an advanced degree.
- Easily deal with the diversity of modeling tasks: univariate, multivariate, and multifactor.
- Use your data in the modern forms collected – text, functional and more – and transform it to build more useful models for better insights.
JMP is the market leader. It utilizes strong multivariate tools, which we need, and every version release comes with new and improved bells and whistles.
Kieran O'Mahony
Data Science and Analytics Manager, Dairygold
Advanced Statistical Modeling With JMP: Highlighted Features
Multivariate analysis
- Principal components analysis (PCA)
- Clustering
- Correlation analysis
- Factor analysis
- Multidimensional scaling
- Structural equation modeling (SEM)
- Discriminant analysis
- Multiple correspondence analysis
- Latent class analysis
- Partial least squares (PLS)
- t-SNE
Nonlinear modeling
- Dissolution
- Nonlinear regression
- Gaussian process model
- Spline fitting
- Fit curve
Time series
- ARIMA
- Seasonality
- Autocorrelation
- Moving average
- Auto regressive (AR)
Advanced regression
- Generalized regression
- Mixed models
- Generalized linear models
- GLMM
- Penalized regression
- Random effects
Variable selection
- Predictor screening
- Variable importance
- Stepwise
- All possible models
- Column contributions
Functional data analysis
- Spectral analysis
- Wavelet analysis
- Smoothing
- Savitzky-Golay smoothing
- Functional DOE
- B-Spline
- P-Spline
- Fourier basis
Process optimization
- Profiling
- Simulation
- Trade space analysis
The best discoveries start with JMP
JMP® Analytic Capabilities
See everything that JMP® can do for you and your organization, from data access and cleaning, to exploration and visualization, all the way through sharing and communicating your results.
