IBM® SPSS® Statistics Professional Edition includes all the capabilities within the Standard Edition plus advanced statistical procedures that address data quality, forecasting, classification and insights into categorical data.
Both novice and experienced users can use advanced features to develop reliable forecasts using time-series data. Use classification and decision trees to help you identify groups and relationships and predict outcomes. Drive more valid conclusions by uncovering missing data patterns, and inputing missing values using SPSS Missing Values. Visualize and explore relationships and predict the values of categorical variables using SPSS Categories.
Harness the power of advanced SPSS Statistics modules
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.
Uncover the patterns behind missing data, estimate summary statistics and impute missing values using statistical algorithms to draw more valid conclusions.
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.
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
- Autoregressive integrated moving average
- Expert modeler exponential smoothing methods
- Forecast multiple series (outcomes) at once
- Temporal causal modeling
- Seasonal decomposition
- Spectral analysis
- Correspondence analysis (ANACOR)
- Principal components analysis for categorical data (CATPCA; replaces PRINCALS)
- Ridge regression, lasso, elastic net (CATREG)
- Nonlinear canonical correlation (OVERALS)
- Multidimensional scaling for individual differences scaling with constraints (PROXSCAL)
- Preference scaling (PREFSCAL; multidimensional unfolding)
- Multiple correspondence analysis
- Data patterns table
- Imputation with means estimation or regression
- Listwise and pairwise statistics
- Missing patterns table
- Multiple imputation of missing data
- Exhaustive CHAID