Uncover data insights that can help solve business and research problems<\/p>\n\t
IBM SPSS\u00ae Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data.<\/p>\n
With SPSS Statistics you can:<\/p>\nAnalyze and better understand your data, and solve complex business and research problems through a user-friendly interface.
\nMore quickly understand large and complex data sets with advanced statistical procedures that help ensure high accuracy and quality decision-making.
\nUse extensions, Python and R programming language code to integrate with open-source software.
\nMore easily select and manage your software with flexible deployment options.\n\t\t\t\t![\"annaly_note\"](\"https:\/\/service.cgtae.com\/cloudeng\/wp-content\/uploads\/2020\/03\/annaly_note.png\" "\\"annaly_note\\"")
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Base provides a wide variety of analytics capabilities including advanced data preparation, descriptive statistics, linear regression, visual graphing and reporting.<\/p>\nKey features:<\/strong> Requires SPSS Statistics Base Subscription.<\/p>\n 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.<\/p>\nKey features:<\/strong> Requires SPSS Statistics Base Subscription.<\/p>\n 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.<\/p>\nKey features:<\/strong> Requires SPSS Statistics Base Subscription.<\/p>\n Unlocks capabilities to predict trends using time-series data, and uncovers relationships using classification, decision trees, and neural networks.<\/p>\nKey features:<\/strong>
\n\u2022 Basic hypothesis testing
\n\u2022 Bootstrapping
\n\u2022 Cluster analysis
\n\u2022 Data access and management
\n\u2022 Data preparation
\n\u2022 Graphs and charts
\n\u2022 Help center
\n\u2022 Linear regression
\n\u2022 Nonparametric tests
\n\u2022 One-way ANOVA
\n\u2022 Output management
\n\u2022 Programmability extension
\n\u2022 ROC analysis
\n\u2022 Support for R\/Python\n\tAdd-on: Custom tables and advanced statistics<\/h5>\n
\n\u2022 2-stage least squares regression
\n\u2022 Bayesian statistics
\n\u2022 Generalized linear mixed models (GLMM)
\n\u2022 Generalized linear modeling (GLM)
\n\u2022 Logistic regression
\n\u2022 Loglinear analysis
\n\u2022 Multivariate analysis
\n\u2022 Nested tables
\n\u2022 Non-linear regression
\n\u2022 Probit response analysis
\n\u2022 Quantile regression
\n\u2022 Repeated measures analysis
\n\u2022 Survival analysis
\n\u2022 Weighted least squares regression\n\tAdd-on: Complex sampling and testing<\/h5>\n
\n\u2022 Categorical principal components analysis
\n\u2022 Conjoint analysis
\n\u2022 Decision trees
\n\u2022 Exact tests
\n\u2022 Missing values
\n\u2022 Multidimensional scaling and unfolding
\n\u2022 Multiple correspondence analysis
\n\u2022 Neural networks
\n\u2022 Regressions with optimal scaling including lasso and elastic net
\n\u2022 Time series analysis\n\tAdd-on: Forecasting and decision trees<\/h5>\n
\n\u2022 ARIMA modeling
\n\u2022 C&RT
\n\u2022 CHAID
\n\u2022 Direct marketing analysis
\n\u2022 Exhaustive CHAID
\n\u2022 Multilayer perception
\n\u2022 Neural networks
\n\u2022 QUEST
\n\u2022 Radial basis function
\n\u2022 Seasonal decomposition
\n\u2022 Spectral analysis
\n\u2022 Temporal causal modeling\n\t