The Unscrambler® combines statistical analysis techniques and “multivariate mapping” for easier data interpretation. This means users can effortlessly perform powerful statistical analysis while easily communicating results to colleagues.

Multivariate Curve Resolution (MCR): Resolves mixtures by determining the number of constituents, their spectral profiles and their estimated concentrations
Descriptive statistics (Mean, Standard Deviation, Box-Plot, skewness, kurtosis, correlation matrix)
Principal Component Analysis (PCA)
Regression (MLR, PCR, PLS-R, 3-way PLS-R)
Prediction from PCR, PLS-R and 3-way PLSR models
Classification (SIMCA, PLS-DA)
ANOVA and Response Surface ANOVA
Validation options: Leverage Correction, Cross-Validation (freely choose number of samples per segment), Test Set
Variable Scaling options: Scaling is free on each variable. Suggested options: Auto-scaling, Constant, Passify
Interaction and Square terms can be included in all models
Smart Tools for Analysis

Analytical tools required to decipher data and create innovative solutions.
Automatic detection of significant X-variables in PCR and PLS-R
Model Stability in PCA, PCR and PLS-R
Automatic outlier detection in PCA, MLR, PCR, PLS-R and Prediction
Message list of recommendations in MCR modeling

Interactive analysis:
Mark samples and/or variables on plots
Recalculate With or Without Marked samples or variables
Recalculate With Passified Marked or Unmarked variables
Extract Data from Marked or Unmarked
Automatic Pretreatments in Prediction and Classification
Tutorial exercises guiding you through the use of all The Unscrambler® modeling techniques in application examples

Design of Experiments
Design Wizard: Takes you through the stages of building a design
Fractional and Full Factorial designs
Plackett-Burman designs
Box Behnken designs
Mixture designs (Simplex-Lattice, Axial, Simplex-Centroid)