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 |
|
|
|
|