Topcateg:9

SPSS Statistics 25 - Data Preparation versions & prices
0

 

Total
incl. 19 % VAT

Quantity

IBM SPSS Data Preparation 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.

Recommended products

STATA 15/MP

STATA 15/MP

STATA 15/MP

A comprehensive statistics program for time series analysis! more details

Download pricelist Product information

SPSS Statistics 25 - Standard

SPSS Statistics 25 - Standard

SPSS Statistics 25 - Standard

SPSS Standard gives you the basic functionality for all kinds of statistical analysis. more details

Download pricelist Product information

Statgraphics Centurion 18 - Multilingual

Statgraphics Centurion 18 - Multilingual

Statgraphics Centurion 18 - Multilingual

Popular statistics program for engineers with more than 230 statistical analysis, graphs, statistical design of... more details

Download pricelist Product information

IBM SPSS Statistics - Data Preperation

Improve data preparation for more accurate results

IBM® SPSS® Data Preparation performs advanced techniques that streamline the data preparation stage of the analytical process to deliver faster, more accurate data analysis results. Analysts can choose from a completely automated data preparation procedure for the fastest results, or select from several other methods to help prepare more challenging data sets.

With this software, you can easily identify suspicious or invalid cases, variables and data values. You can also view patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.

SPSS Data Preparation helps:

  • Automate the data preparation process—to eliminate complex, time-consuming manual data preparation.
  • Validate data without manual checks—to perform faster, more accurate data validation.
  • Prevent outliers from skewing analyses—to automatically detect anomalies that could corrupt results.

Desktop-Systems

  Windows® Mac® OS X Linux®
Further Requirements Super VGA-Monitor (800x600) or higher Resolution
For a connection to SPSS Statistics Base Server, you will need a network adapter for TCP/IP-Network protocol
Internet Explorer
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Operating System Windows XP, Vista, 7, 8, 10 (32-/64-Bit) Mac OS X 10.7 (32-/64-Bit), Mac OS X 10.8 (only 64-Bit!) Debian 6.0 x86-64, Red Hat Enterprise Linux (RHEL) 5 Desktop Editions, Red Hat Enterprise Linux (RHEL) Client 6 x86-64:
  • Linux (64 bit) kernel 2.6.28-238.e15 or higher
  • FORTRAN version libgfortran.so.3
  • C++ Version libstdc++.so.6.0.10
Min. CPU Intel or AMD-x86-Processor 1 GHz or better Intel-Processor (32-/64-Bit) Intel or AMD-x86-Processor 1 GHz or better  
Min. RAM 1 GB RAM + 1 GB RAM + 1 GB RAM +
Festplattenplatz Min. 800 MB Min. 800 MB Min. 800 MB

Server-Systems

  SPSS Statistics Server
Further Requirements For Windows-, Solaris-PC's: Network adapter with TCP/IP-Network protocol
For System z-PC's: OSA-Express3 10 Gigabit Ethernet, OSA-Express3 Gigabit Ethernet, OSA-Express3 1000BASE-T Ethernet
Operating System Windows Server 2008 or 2012 (64-Bit), Red Hat Enterprise Linux 5 (32-/64-Bit), SUSE Linux Enterprise Server 10 and 11 (32-/64-Bit)

Details can be found in the the following PDF-document:System Requirements SPSS Statistics Server 22
Min. CPU  
Min. RAM 4 GB RAM +
Disk Space ca. 1 GB for the installation. Double the amount may be needed.

Automate the data preparation process

  • Prepare data in a single step.
  • Detect and correct quality errors and impute missing values.
  • Quickly determine which data to use in your analysis.
  • View easy-to-understand reports with recommendations and visualizations.

Validate data without manual checks

  • Ensure consistency of data validation from project to project.
  • Apply validation rules based on each variable’s measure level (categorical or continuous).
  • Receive reports of invalid cases, rule violation summaries and number of cases affected.
  • Remove or correct suspicious cases at your discretion before analysis.

Prevent outliers from skewing analyses

  • Search for unusual cases based upon deviations from similar cases.
  • Flag outliers by creating a new variable.
  • Examine unusual cases to determine if they should be included in analyses.