TIBCO Statistica® Correlation
Explore correlations and partial correlations between variables with Statistica. Common measures of association can be computed, including Pearson r, Spearman rank order R, Kendall tau (b, c), Gamma, tetrachoric r, Phi, Cramer V, contingency coefficient C, Sommer's D, uncertainty coefficients, part and partial correlations, autocorrelations, various distance measures, etc... Nonlinear regressions, regressions for censored data and other specialized measures of correlations are available in Nonlinear Estimation, Survival Analysis.
Correlation matrices can be computed using casewise (listwise) or pairwise deletion of missing data, or mean substitution.
Brushing and Outlier Detection
The brushing facilities in the scatterplots allow the user to select/deselect individual points in the plot and assess their effect on the regression line or other fitted function lines.
Numerous visualizations are available to further study patterns of relationships between variables, e.g., 2D and 3D scatterplots (with or without case labels). This is designed to identify patterns of relations across subsets of cases or series of variables.
Color maps can be generated. The magnitude or statistical significance of a correlation coefficient in a correlation matrix can be quickly identified by the background color of the respective cell.
Correlation matrices can be computed as categorized by grouping variables and visualized via categorized scatterplots. Also "breakdowns of correlation matrices" can be generated (one matrix per subset of data), displayed in queues of Spreadsheets, and saved as stacked correlation matrices. This can be used as input into the Structural Equations Modeling and Path Analysis (SEPATH) module. An entire correlation matrix can be summarized in a single graph via the Matrix scatterplot option.
Categorized scatterplot matrix plots can be generated; one matrix plot for each subset of data. Alternatively, a multiple-subset scatterplot matrix plot can be created where specific subsets of data (e.g., defined by levels of a grouping variable or selection conditions of any complexity) are marked with distinctive point markers. Various other graphical methods can be used to visualize matrices of correlations in search of global patterns (e.g., contour plots, non-smoothed surfaces, icons, etc.).