Overview
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.
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
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
- C&RT
- CHAID
- Exhaustive CHAID
- QUEST