New in Minitab Web-App 20.2 Correlogram for visual comparison of Pearson correlation coefficients between each pair of variables Enhancements for variable weighting in CART® classification and CART® regression Enhancements for scatter plot, heat map and parallel coordinate plot Optional chargeable Predictive Analytics Module including TreeNet® classification, TreeNet® regression, Random Forests® classification and Random Forests®regression Possibility to log in to your Minitab account via Facebook or Google Correlogram The new correlogram in Minitab 20.2 can be used to visually compare Pearson correlation coefficients between pairs of variables. Minitab 20.2 uses a color gradient to indicate low and high correlation coefficient values, making it easy to identify the most and least important relationships. Predictive Analytics – optional, chargeable Add-On Module The chargeable additional module 'Prective Analytics' enhances Minitab with machine learning algorithms, TreeNet® and Random Forests®. Tree-based methods map non-linear relationsships quite well. The TreeNet® modeling engine's level of accuracy is usually not attainable by single models or by ensembles such as bagging or conventional boosting. The TreeNet methodology is not sensitive to data errors and needs no time-consuming data preparation. Random Forests®' strengths are spotting outliers and anomalies in data, displaying proximity clusters, predicting future outcomes, identifying important predictors, discovering data patterns, replacing missing values with imputations, and providing insightful graphics. More information on the Minitab Module "Predictive Analytics"