Tools to address the challenges of the entire analytic life cycle
The IBM SPSS Statistics Professional Edition goes beyond the core statistical capabilities offered in the Standard Edition to address issues of data quality, data complexity, automation and forecasting. It is designed for users who perform many types of in-depth and non-standard analyses and who need to save time by automating data preparation tasks.
The IBM SPSS Statistics Professional edition includes the following key capabilities:
- Linear models offer a variety of regression and advanced statistical procedures designed to fit the inherent characteristics of data describing complex relationships.
- Nonlinear models provide the ability to apply more sophisticated models to data.
- Simulation capabilities help analysts automatically model many possible outcomes when inputs are uncertain, improving risk analysis and decision making.
- Customized tables enable users to easily understand their data and quickly summarize results in different styles for different audiences.
- Data preparation streamlines the data preparation stage of the analytical process.
- Data validity and missing values increase the chance of receiving statistically significant results.
- Decision trees make it easier to identify groups, discover relationships between groups and predict future events.
- Forecasting features enable you to analyze historical data and predict trends faster.
- SPSS Statistics Professional includes generalized linear mixed models (GLMM) for use with hierarchical data.
- This software has general linear models (GLM) and mixed models procedures.
- It includes generalized linear models (GENLIN), including widely used statistical models such as linear regression for normally distributed responses, logistic models for binary data, and loglinear models for count data. GENLIN also offers many useful statistical models through its very general model formulation.
- Generalized estimating equations (GEE) procedures extend generalized linear models to accommodate correlated longitudinal data and clustered data.
- Multinomial logistic regression (MLR) predicts categorical outcomes with more than two categories.
- Binary logistic regression classifies data into two groups.
- Nonlinear regression (NLR) and constrained nonlinear regression (CNLR) estimate parameters of nonlinear models.
- Probit analysis evaluates the value of stimuli using a logit or probit transformation of the proportion responding.
- 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.
- The parameters used can be modified to simulate the data and compare outcomes.
- Specifications for a simulation can be saved to a simulation plan file.
- Simulations may be run using specifications from a loaded simulation plan file. Users can also provide specifications in the user interface and run the simulation from the interface.
- Means or proportions are compared for demographic groups, customer segments, time periods or other categorical variables when including inferential statistics.
- The software creates summary statistics—from simple counts for categorical variables to measures of dispersion—and sorts categories by any summary statistic used.
- It includes three significance tests: Chi-square test of independence, comparison of column means (t test) or comparison of column proportions (z test).
- An interactive table builder provides drag and drop capabilities for creating pivot tables.
- It excludes specific categories, displays missing value cells and can add subtotals to tables.
- Tables can be previewed in real time and modified as they are created. They are exportable to Microsoft Word, Excel, PowerPoint or HTML for use in reports.
- SPSS Statistics Professional identifies suspicious or invalid cases, variables and data values.
- The software lets you view patterns of missing data and summarize variable distributions.
- Optimal Binning finds the best possible outcome for algorithms designed for nominal attributes.
- The Automated Data Preparation (ADP) tool detects and corrects quality errors and imputes missing values in one efficient step.
- Recommendations and visualizations help you determine which data to use.
Data validity and missing values
- SPSS Statistics Professional examines data from several different angles using one of six diagnostic reports, then estimates summary statistics and imputes missing values.
- It diagnoses serious missing data imputation problems.
- The software replaces missing values with estimates.
- It displays a snapshot for each type of missing value and any extreme values for each case.
- Hidden bias is removed by replacing missing values with estimates to include all groups—even those with poor responsiveness.
- SPSS Statistics Professional visually determines how your model flows so you can find specific subgroups and relationships.
- The software creates classification trees directly within IBM SPSS Statistics so you can use results to segment and group cases directly within the data.
- It includes four established tree-growing algorithms:
- CHAID—A fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome.
- Exhaustive CHAID—A modification of CHAID, which examines all possible splits for each predictor.
- Classification and regression trees (C&RT)—A complete binary tree algorithm, which partitions data and produces accurate homogeneous subsets.
- QUEST—A statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.
- Selection or classification/prediction rules are generated in IBM SPSS Statistics syntax, SQL statements or simple text (through syntax).
- SPSS Statistics Professional enables you to deliver information in ways that your organization’s decision-makers can understand and use.
- It automatically determines the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
- Hundreds of different time series can be modeled at once, rather than one variable at a time.
- Models are saved to a central file so that forecasts can be updated when data changes without having to re-set parameters or re-estimate models.
- Scripts can be written to update models with new data automatically.
Tools to address the challenges of the entire analytic life cycle
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