Adjust the parameters used to simulate the data and compare outcomes.Simulate data according to user-specified parameters, and use simulated data as input to predict an outcome.Assess “what if” scenarios to predict outcomes of different business decisions.The latest version of SPSS Statistics Standard brings you:
Ibm spss statistics software#
In addition, the software includes enhancements that enable users to program in Java, import more types of data, enhance security and more. IBM SPSS Statistics Standard delivers new simulation modeling techniques to help you make better decisions and assess risk under uncertain conditions. SPSS Statistics Standard improves decision making and risk analysis with latest release
Tables are exportable to Microsoft® Word, Excel®, PowerPoint® or HTML for use in reports. Tables can be previewed in real time and modified as they are created. It excludes specific categories, displays missing value cells and can add subtotals to tables. An interactive table builder provides drag and drop capabilities for creating pivot tables. It includes three significance tests: Chi-square test of independence, comparison of column means (t test), or comparison of column proportions (z test). The software creates summary statistics - from simple counts for categorical variables to measures of dispersion – and sorts categories by any summary statistic used. Means or proportions are compared for demographic groups, customer segments, time periods or other categorical variables when including inferential statistics. Users can also provide specifications in the user interface and run the simulation from the interface. Simulations may be run using specifications from a loaded simulation plan file. Specifications for a simulation can be saved to a simulation plan file. The parameters used can be modified to simulate the data and compare outcomes. Monte Carlo techniques provide the ability to simulate data according to parameters you specify, and then use that simulated data as input for predicting an outcome. Probit analysis evaluates the value of stimuli using a logit or probit transformation of the proportion responding. Nonlinear regression (NLR) and constrained nonlinear regression (CNLR) estimate parameters of nonlinear models. Binary logistic regression classifies data into two groups. Multinomial logistic regression (MLR) predict categorical outcomes with more than two categories. Generalized estimating equations (GEE) procedures extend generalized linear models to accommodate correlated longitudinal data and clustered data. GENLIN also offers many useful statistical models through its very general model formulation. It includes generalized linear models (GENLIN), including widely used statistical models such as linear regression for mormally distributed responses, logistic models for binary data, and loglinear models for count data. This software has general linear models (GLM) and mixed models procedures. 
Statistics Standard includes generalized linear mixed models (GLMM) for use with hierarchical data.The IBM SPSS Statistics Standard edition includes the following key capabilities:
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