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 | | | | |