IBM SPSS ANALYTICS
Enabling customers to bring data to the heart of decision making.
Why IBM SPSS Statistics?
IBM SPSS Statistics is an integrated family of products that addresses the entire analytical process, from planning to data collection to analysis, reporting and deployment. With more than a dozen fully integrated modules to choose from, you can find the specialised capabilities you need to increase revenue, outperform competitors, conduct research and make better decisions.
A comprehensive set of statistical tools
Work inside a single, integrated interface to run descriptive statistics, regression, advanced statistics and many more. Create publication ready charts, tables, and decision trees in one tool.
Integration with Open Source
Enhance the SPSS Syntax with R and Python through specialized extensions. Distribute integrated R or Python packages to a wide range of users who are not familiar with R or Python, access to over 4,000 open source statistical functions.
Easy statistical analysis
Use a simple drag and drop interface to access a wide range of capabilities and work across multiple data sources. Plus, flexible deployment options make purchasing and managing your software easy.
IBM SPSS Statistics Packages
IBM SPSS Statistics is a modular product and available in three pre-packaged editions. Which edition is best for you depends on the type of analysis you’re doing. If you would like advice on this, just get i touch and we will be happy to help.
Fundamental analytical capabilities for a wide variety of business and research questions
Additional capabilities to address issues of data quality, data complexity, automation and forecasting
A full range of analytical techniques plus structural equation modeling (SEM). In-depth sampling assessment and testing, and procedures for direct marketing.
IBM SPSS Statistics Modules
SPSS Statistics is a modular product. You can select the combination of modules that best meet your requirements.
Std Modules included in IBM SPSS Standard
Pro Modules included in IBM SPSS Professional
Prem Modules included in IBM SPSS Premium
IBM SPSS Statistics Base Std
Forms the foundation for many types of statistical analyses, allowing a quick look at data and its easy preparation for analysis. Easily build charts with sophisticated reporting capabilities, formulate hypotheses for additional testing, clarify relationships between variables, create clusters, identify trends and make predictions.
The following modules can be added to IBM SPSS Statistics Base.
These are the most popular modules bought together with IBM SPSS Statistics Base:
Dive deeper into your data, analyse variances and the complex relationships of real world data to draw more dependable conclusions.
When your data does not conform to the assumptions required by standard analytical procedures, apply more sophisticated univariate and multivariate analytical techniques.
When there is no clear distinction between independent or dependent variables, loglinear and hierarchical loglinear analysis can be used for modelling multiway tables of count data.
Examine the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems with state-of-the-art survival procedures; Kaplan-Meier and Cox regression.
Predict nonlinear outcomes, such as ordinal values or what product a customer is likely to buy, by using generalized linear mixed models (GLMM).
Model means, variances and covariances in your data using the general linear models (GLM). Describe the relationship between a dependent variable and a set of independent variables. Build flexible models including linear regression, ANOVA, ANCOVA, MANOVA, and MANCOVA. GLM also includes capabilities for repeated measures, mixed models, post hoc tests and post hoc tests for repeated measures, four types of sums of squares, and pairwise comparisons of expected marginal means, as well as the sophisticated handling of missing cells, and the option to save design matrices and effect files.
Use generalised linear models (GENLIN) and accommodate correlated longitudinal data and clustered data with generalised estimating equations (GEE).
Advanced Statistics techniques are commonly used to:
- Identify product interest levels and the impact of customer satisfaction.
- Analyse different medications and their effectiveness.
- Determine ways to repair processes or make improvements to existing processes.
Use Decision Trees for better profiling and targeting. Identify groups, discover relationships between them and predict future events.
Using the comprehensive interface, you can easily build highly visual classification trees to uncover relationships, segments and patterns.
Present groupings in a highly visual and intuitive manner, perfect for non-technical audiences. Display tree diagrams, tree maps, bar graphs and data tables. Choose which statistics, charts and rules to include.
Prune your tree and refine your model, by collapsing and expanding branches.
Dig deeper into your data as visual results can help you find specific subgroups and relationships that you might not uncover using more traditional statistics. Run further analysis on these subgroups and save information from trees as new variables for deeper insights.
Choose from four tree-growing algorithms – CHAID, Exhaustive CHAID, C&RT and QUEST, to find the best fit for your data.
Evaluate your model by using the gains summary tables and gains chart to identify segments by highest (and lowest) contribution.
Directly select cases or assign predictions in your data from the model results, or export rules for later use.
Classification and decision trees are commonly used for:
- Data reduction and variable screening
- Interaction identification
- Category merging
- Discretizing continuous variables
Summarise data and display your analyses as presentation-quality, production ready tables.
The Custom Tables module allows you to build tables and charts in a succinct and clear way with full control over what goes into your reports and how they’re styled. Advanced analytical features allow you to learn from your data and build tables that are easy to read and interpret.
Preview tables as you build them with the drag-and-drop functionality and make changes in real-time.
Include multiple variables and describe your data in multiple structures, cross tabulation and pivot table options as well as additional descriptive statistics.
Present table results using nesting, stacking and multiple response categories as well as continuous measurement fields.
Customise layout and format by collapsing categories, swapping row and column variables, and editing labels directly on the table.
Identify differences, changes or trends in the data by running more than 160 summary statistics, subtotals and displaying missing value cells. Calculate statistics for each cell, row, column, subgroup or table, and highlight cells with significance test results.
Create new fields directly in output tables to perform calculations on output categories and build bar charts using the values produced within the tables.
Protect respondents’ identities by hiding cells or excluding categories.
Automate frequent tasks to automatically produce similar tables with new data.
Custom Tables are used for:
- Easily creating visually appealing tables.
- Presenting and sharing complex information in a clear and succinct format.
- Saving time by automating frequent tables.
- Producing tables for publication/reports.
Predict categorical outcomes and apply various nonlinear regression procedures where ordinary regression techniques are limiting or inappropriate.
Free yourself from constraints such as yes/no answers with Multinomial Logistic Regression (MLR). Model which factors predict if customers buy product A, product B or product C.
Easily classify your data into two groups such as buy or not buy or vote or not vote using Binary Logistic Regression. Select the main and interaction effects that best predict your response variable with stepwise methods.
When your data does not meet the statistical assumptions for ordinary least squares, give more weight to measurements within a series by using weighted least squares (WLS) or two-stage least squares (2SLS) to control for correlations between predictor variables and error terms that often occur with time-based data.
Have more control over your model by using constrained and unconstrained nonlinear regression procedures.
Analyse the potency of responses to stimuli, such as medicine doses, prices or incentives with probit and logit response modelling.
Choose from four methods of selecting predictors: forward entry, backward elimination, forward stepwise and backward stepwise.
Regression techniques are commonly used for:
- Investigating consumer buying habits
- Credit risk analysis
- Supply and demand impact analysis
Other IBM SPSS Statistics Modules
Gives analysts advanced techniques to streamline the data preparation stage of the analytical process, prior to analysis. While basic data preparation tools are included in IBM SPSS Statistics Base, IBM SPSS Data Preparation provides specialized techniques to prepare your data for more accurate analyzes and results