Posts Tagged ‘predictive analytics’

Ranking A ranking is a relationship between a set of items such that, for any two items, the first is either “ranked higher than”, “ranked lower than” or “ranked equal to” the second. – Wikipedia   Ranking in SPSS Statistics IBM SPSS Statistics ranks cases in your data pond by automatically defining new variables to […]

The SPSS Statistics Crosstabs procedure forms two-way and multi-way tables (and provides a variety of tests and measures of association for the two-way tables). The structure of the table and whether categories are ordered determine what test or measure to use. Crosstabs’ statistics and measures of association are computed for two-way tables only. If you […]

A  codebook is a type of document used for gathering and storing codes. Originally codebooks were often literally books, but today codebook is a byword for the complete record of a series of codes, regardless of physical format. – Wikipedia The codebook command was introduced in IBM SPSS Statistics version 17. It provides information about the […]

IBM SPSS Statistics offers many ways to help save time when analyzing data, particularly if you are continually performing the same types of analysis on similar sets of pools of data. TIME SAVERS “Metadata Attributes” – Data attributes have properties associated with them, and these properties are defined in metadata.   During data analysis, documentation […]

Knowledge Bottleneck? When building a predictive model, the larger the number of examples or “cases” considered, the better the model. Typically, these cases exist in multiple data files (or data sources) that must be “stitched together”. The task of accessing each data source, performing some analysis on the cases contained in the data and formatting […]

Over the last month I’ve been taking a tactical view of analytics by focusing on some of the specific features of IBM SPSS Statistics so today, I have decided to think a bit more “strategically”. If your organization wants to begin leveraging survey-response type data for example, what might be a reasonable approach? If I […]

The power of SPSS allows the data scientist or predictive modeler to consume large data volumes. This data may come in smaller manageable subsets or possible huge “data ponds”.  Depending upon the procedures you will be performing in your analysis, SPSS may reread the entire data set for each procedure.  Of course, procedures that change […]

  Serious Analytical Architect? Any serious analytic architect will need to at least be aware of the individual SPSS products offered and have at least a basic understanding of what each of them can do. Here is the list: IBM Showcase Report Writer Quickly create professional-looking, presentation-quality reports using intuitive, word processor-like page layout and […]

IBM SPSS defines each variable with a “TYPE”. By default, all variables in SPSS are assumed to be numeric until you change them. SPSS V20 currently supports the following variable types: Numeric and String (the most common), Comma, Dot, Scientific Notation, Date, Dollar, Custom Currency and Restricted Numeric What day is it? Today I want […]

IBM SPSS Statistics – Data Management Toolset (DMS) In a recent blog post I listed some of the more helpful “data management tools” offered within IBM SPSS Statistics version 20 (Case Summaries, Replace Missing Values, Transform and Compute, Recode, Select Cases, Sort Cases and Merge Files) and would like to review them today. These tools […]

If you work within the rapidly expanding analytics space, you will need to think about defining and sharing statistical models between applications. PMML (or Predictive Model Markup Language) is an XML-based language developed by the Data Mining Group (DMG) for this purpose. I’d like to pass on some of the essentials: The Basics PMML provides […]

Last Thursday Perficient held a webinar led by Curtis Mahanay who is a Senior Functional Consultant and accountant. The webinar was entitled, “Deeper Insight into Financials with a Driver-Based Cost & Profitability Model“. He presented on the important topic of turning data into information that capital intensive organizations, like hospitals, can use to maintain efficiency, […]