IBM SPSS Statistics
Uncover data insights that can help solve business and research problems
Why SPSS Statistics?
IBM SPSS® Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data.
With SPSS Statistics you can:
Analyze and better understand your data, and solve complex business and research problems through a user-friendly interface.
More quickly understand large and complex data sets with advanced statistical procedures that help ensure high accuracy and quality decision-making.
Use extensions, Python and R programming language code to integrate with open-source software.
More easily select and manage your software with flexible deployment options.
Features
SPSS Statistics Base Subscription
Base provides a wide variety of analytics capabilities including advanced data preparation, descriptive statistics, linear regression, visual graphing and reporting.
Key features:
• Basic hypothesis testing
• Bootstrapping
• Cluster analysis
• Data access and management
• Data preparation
• Graphs and charts
• Help center
• Linear regression
• Nonparametric tests
• One-way ANOVA
• Output management
• Programmability extension
• ROC analysis
• Support for R/Python
Add-on: Custom tables and advanced statistics
Requires SPSS Statistics Base Subscription.
This is our most popular add-on. Allows users to predict categorical outcomes, apply non-linear regression, perform multivariate modeling, and summarize findings through custom tables.
Key features:
• 2-stage least squares regression
• Bayesian statistics
• Generalized linear mixed models (GLMM)
• Generalized linear modeling (GLM)
• Logistic regression
• Loglinear analysis
• Multivariate analysis
• Nested tables
• Non-linear regression
• Probit response analysis
• Quantile regression
• Repeated measures analysis
• Survival analysis
• Weighted least squares regression
Add-on: Complex sampling and testing
Requires SPSS Statistics Base Subscription.
Enables users to work with complex sample designs, uncover missing data, apply categorical regression procedures, understand consumer preferences, and work more accurately with small samples.
Key features:
• Categorical principal components analysis
• Conjoint analysis
• Decision trees
• Exact tests
• Missing values
• Multidimensional scaling and unfolding
• Multiple correspondence analysis
• Neural networks
• Regressions with optimal scaling including lasso and elastic net
• Time series analysis
Add-on: Forecasting and decision trees
Requires SPSS Statistics Base Subscription.
Unlocks capabilities to predict trends using time-series data, and uncovers relationships using classification, decision trees, and neural networks.
Key features:
• ARIMA modeling
• C&RT
• CHAID
• Direct marketing analysis
• Exhaustive CHAID
• Multilayer perception
• Neural networks
• QUEST
• Radial basis function
• Seasonal decomposition
• Spectral analysis
• Temporal causal modeling