Overview

Feature highlights

Harness the power of advanced SPSS Statistics modules

Forecasting

Develop reliable forecasts, regardless of the size of the data set or number of variables. Advanced time-series modeling procedures help you create forecasts quickly.

Missing values

Uncover the patterns behind missing data, estimate summary statistics and impute missing values using statistical algorithms to draw more valid conclusions.

Categories

Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and ordered or unordered categorical predictor variables.

Decision trees

Create visual classification and decision trees to identify groups or predict values of a target variable. Enables you to predict or classify future observations based on a set of decision rules.

Professional Edition features

Forecasting

  • Autoregressive integrated moving average
  • Autoregression
  • Expert modeler exponential smoothing methods
  • Forecast multiple series (outcomes) at once
  • Temporal causal modeling
  • Seasonal decomposition
  • Spectral analysis

Categories

  • Correspondence analysis (ANACOR)
  • Principal components analysis for categorical data (CATPCA; replaces PRINCALS)
  • Ridge regression, lasso, elastic net (CATREG)
  • CORRESPONDENCE
  • Nonlinear canonical correlation (OVERALS)
  • Multidimensional scaling for individual differences scaling with constraints (PROXSCAL)
  • Preference scaling (PREFSCAL; multidimensional unfolding)
  • Multiple correspondence analysis

Missing values

  • Data patterns table
  • Imputation with means estimation or regression
  • Listwise and pairwise statistics
  • Missing patterns table
  • Multiple imputation of missing data
  • Pooling

Decision trees