| Statistics | Systat Version |
 | 13.2 | 12.0 | 11.0 | 10.2 |
| Trimmed Mean – Row & Column |  |  | | |
| | Standard Error | | | | |
| Confidence Interval | | | | |
| Windsorized Mean – Row & Column | | | | |
| | Standard Error | | | | |
| Confidence Interval | | | | |
| Probability calculator | | | | |
| Mode – Row | | | | |
| | Interquartile Range | | | | |
| Random sampling | | | | |
| Univariate discrete and continuous distributions | | | | |
| Multivariate distributions * | | | | |
| Design of experiments | | | | |
| Power analysis | | | | |
| Descriptive Statistics | | | | |
| Column | | | | |
| Row | | | | |
| N-tiles, P-tiles | | | | |
| Fitting distributions | | | | |
| | Crosstabulation and measures of association | | | | |
| List layouts, list first n levels, display rows with zero counts | | | | |
| Mode for one-way tables | | | | |
| Correspondence analysis | | | | |
| Simple | | | | |
| Multiple | | | | |
| Loglinear models | | | | |
| Nonparametric tests | | | | |
| | Jonckheere-Terpstra | | | | |
| Fligner-Wolfe | | | | |
| Dwass-Steel-Critchlow-Fligner and Conover-Inman | | | | |
| Kruskal-Wallis | | | | |
| Two-sample Kolmogorov-Smirnov | | | | |
| Sign | | | | |
| Wilcoxon signed rank | | | | |
| Friedman | | | | |
| Quade | | | | |
| One-sample Kolmogorov-Smirnov | | | | |
| Anderson-Darling | | | | |
| Wald-Wolfowitz runs | | | | |
| Multinormal tests | | | | |
| Hypothesis Testing | | | | |
| Mean | | | | |
| Variance | | | | |
| Correlation | | | | |
| Proportion | | | | |
| Bootstrap-based p-values for all tests for mean and variance | | | | |
| One and two sample Hotelling T2 test for mean vector of multivariate data | | | | |
| Correlations, distances and similarities | | | | |
| Set and canonical correlations | | | | |
| Cronbachs alpha | | | | |
| Linear regression | | | | |
| | Save standard errors, confidence intervals | | | | |
| Least squares | | | | |
| Bayesian | | | | |
| Ridge | | | | |
| Best subsets | | | | |
| | Find the best models given the number of predictors
Best model by R2, Adjusted R2, Mallow’s Cp, MSE, AIC, AICc and BIC | | | | |
| Polynomial | | | | |
| | Single independent variable up to order 8, Natural and orthogonal methods
Goodness-of fit-statistics (R2 and adjusted R2) and ANOVA with p-values for all models down to linear
Quick Graphs: Confidence and prediction interval plots along with estimates, and a plot of residuals versus predicted values | | | | |
| Robust regression | | | | |
| Least Absolute Deviation (LAD) | | | | |
| M | | | | |
| Least Median of Squares (LMS) | | | | |
| Least Trimmed Squares (LTS) | | | | |
| Scale (S) | | | | |
| Rank | | | | |
| Logistic regression | | | | |
| | Binary, multinomial, discrete choice and conditional through separate simplified interfaces and input data formats | | | | |
| Specify the reference level for binary and multinomial response models | | | | |
| Probit analysis | | | | |
| Partial least squares regression | | | | |
| Two stage least squares regression | | | | |
| Mixed Regression | | | | |
| Smooth and plot | | | | |
| Nonlinear regression | | | | |
| ANOVA | | | | |
| | Options to test normality and homoscedasticity assumptions, including Levene’s test based on median | | | | |
| MANOVA | | | | |
| General Linear Model | | | | |
| Mixed model analysis | | | | |
| Discriminant analysis | | | | |
| Classical Discriminant Analysis (Linear or quadratic) | | | | |
| Robust Discriminant Analysis (Linear or quadratic) | | | | |
| Cluster analysis | | | | |
| Hierarchical | | | | |
| K-means | | | | |
| Additive trees | | | | |
| Factor analysis | | | | |
| Confirmatory Factor Analysis | | | | |
| | Maximum likelihood, Generalized Least-Squares, and Weighted Least-Squares methods of estimation of parameters of the CFA model | | | | |
| Goodness-of-Fit Index (GIF), Root Mean Square Residual (RMR), Parsimonious Goodness-of- Fit Index (PGFI), AIC, BIC, McDonald’s Measure of Certainty, and Non-Normal Fit Index (NNFI) to measure the degree of conformity of the postulated factor model to the data | | | | |
| |
| Time series | | | | |
| | ARCH models: BHHH, BFGS, and Newton-Raphson implementations, forecasts for error variances using the parameter estimates, Jarque-Bera test for normality of errors, McLeod and Lagrange Multiplier tests for ARCH effect | | | | |
| GARCH models: BHHH, BFGS, and Newton-Raphson implementations, forecasts for error variances using the parameter estimates, Jarque-Bera test for normality of errors, McLeod and Lagrange Multiplier tests for ARCH effect | | | | |
| Time series plot | | | | |
| ACF, PACF, CCF | | | | |
| Transform | | | | |
| Moving average, LOWESS, exponential, smoothing | | | | |
| Seasonal adjustment | | | | |
| ARIMA | | | | |
| Trend analysis | | | | |
| Fourier transformation | | | | |
| Missing value analysis | | | | |
| Quality analysis | | | | |
| Histogram | | | | |
| Pareto chart | | | | |
| Box-and-Whisker Plot | | | | |
| Process capability analysis | | | | |
| Control charts | | | | |
| Survival analysis | | | | |
| Response surface methods | | | | |
| Path analysis (RAMONA) | | | | |
| Conjoint analysis | | | | |
| Multidimensional scaling | | | | |
| Perceptual mapping | | | | |
| Partially Ordered Scalogram Analysis with Coordinates (POSAC) | | | | |
| Test item analysis | | | | |
| Signal detection analysis | | | | |
| Spatial statistics | | | | |
| Classification and regression trees | | | | |
| Monte Carlo (Add-on) | | | | |
| IID Monte Carlo * | | | | |
| Rejection sampling * | | | | |
| Adaptive Rejection Sampling (ARS) * | | | | |
| Markov Chain Monte Carlo (MCMC) algorithms * | | | | |
| Metropolis-Hastings (M-H) algorithm * | | | | |
| Gibbs sampling algorithm * | | | | |
| Monte Carlo integration * | | | | |
| Quality analysis (Add-on) | | | | |
| Gauge R & R studies * | | | | |
| Sigma measurements * | | | | |
| Taguchi’s on-line SPC * | | | | |
| Signal-to-Noise ratio analysis of Taguchi loss functions * | | | | |
| Environment Variables – Column | | | | |
