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)