GNU PSPP- The Open Source SPSS

If you are SPSS user (for statistics/ not data mining) you can also try 0ut GNU PSPP- which is the open source equivalent and quite eerily impressive in performance. It is available at or and you can also read more at

PSPP is a program for statistical analysis of sampled data. It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions.

[ Image of Variable Sheet ]The most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package.

PSPP can perform descriptive statistics, T-tests, linear regression and non-parametric tests. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.

A brief list of some of the features of PSPP follows:

  • Supports over 1 billion cases.
  • Supports over 1 billion variables.
  • Syntax and data files are compatible with SPSS.
  • Choice of terminal or graphical user interface.
  • Choice of text, postscript or html output formats.
  • Inter-operates with GnumericOpenOffice.Org and other free software.
  • Easy data import from spreadsheets, text files and database sources.
  • Fast statistical procedures, even on very large data sets.
  • No license fees.
  • No expiration period.
  • No unethical “end user license agreements”.
  • Fully indexed user manual.
  • Free Software; licensed under GPLv3 or later.
  • Cross platform; Runs on many different computers and many different operating systems.

PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data.



This software provides a basic set of capabilities: frequencies, cross-tabs comparison of means (T-tests and one-way ANOVA); linear regression, reliability (Cronbach’s Alpha, not failure or Weibull), and re-ordering data, non-parametric tests, factor analysis and more.

At the user’s choice, statistical output and graphics are done in asciipdfpostscript or html formats. A limited range of statistical graphs can be produced, such as histogramspie-charts and np-charts.

PSPP can import GnumericOpenDocument and Excel spreadsheetsPostgres databasescomma-separated values– and ASCII-files. It can export files in the SPSS ‘portable’ and ‘system’ file formats and to ASCII files. Some of the libraries used by PSPP can be accessed programmatically; PSPP-Perl provides an interface to the libraries used by PSPP.


The PSPP project (originally called “Fiasco”) is a free, open-source alternative to the proprietary statistics package SPSS. SPSS is closed-source and includes a restrictive licence anddigital rights management. The author of PSPP considered this ethically unacceptable, and decided to write a program which might with time become functionally identical to SPSS, except that there would be no licence expiry, and everyone would be permitted to copy, modify and share the program.

Release history

  • 0.7.5 June 2010
  • 0.6.2 October 2009
  • 0.6.1 October 2008
  • 0.6.0 June 2008
  • August 2007
  • 0.4.0 August 2005
  • 0.3.0 April 2004
  • 0.2.4 January 2000
  • 0.1.0 August 1998

Third Party Reviews

In the book “SPSS For Dummies“, the author discusses PSPP under the heading of “Ten Useful Things You Can Find on the Internet” [1]. In 2006, the South African Statistical Association presented a conference which included an analysis of how PSPP can be used as a free replacement to SPSS [2].


Please send FSF & GNU inquiries to There are also other ways to contact the FSF. Please send broken links and other corrections (or suggestions) to

Copyright © 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007 Free Software Foundation, Inc., 51 Franklin St – Suite 330, Boston, MA 02110, USA – Verbatim copying and distribution of this entire article are permitted worldwide, without royalty, in any medium, provided this notice, and the copyright notice, are preserved.

R Oracle Data Mining

Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here and here . Also there is a contest for creative use of R and ODM.

R Interface to Oracle Data Mining

The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

R-ODM is especially useful for:

  • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
  • Scripting of “production” data mining methodologies
  • Customizing graphics of ODM data mining results (examples: classificationregressionanomaly detection)

The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see


Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

Consider highlighting a creative use of R and ODM.

BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

Also an interview with Oracle Data Mining head, Charlie Berger

R Graphics

A great book for R graphics is here. Its especially useful for people who are new into R and or using graphical function primarily.


This is a good textbook till the new edition November 2008 release of Bob Munchien’s R for SAS and SPSS Users ( )

The existing free copy is at



Dude , Wheres my software ?

Here is the reason why .

It would take an average Indian 26 months to buy a software worth 6000 USD (Assuming he didnot spend any money on anything else) while it takes the average UK  citizen only 2 months.

But why Sweden ……see post here on a Swedish Website

Here is a list of countries by per capita GDP in terms of purchasing power parity

(or how much they make on an year).













ROC Curve

ROC Curve is a nice modeling concept to know as it will used practically in nearly all models

irrespective of spoefic technique and irrespective of statistical software.

We use the Wikipedia for referring to easy to implement statistics rather than crusty

thick books which seem prohibitely dense and opaque to outsiders

-This is how you define the ROC Curve.

actual value
p n total
p’ True
n’ False
total P N

true positive (TP)

eqv. with hit
true negative (TN)
eqv. with correct rejection
false positive (FP)
eqv. with false alarm, Type I error
false negative (FN)
eqv. with miss, Type II error
true positive rate (TPR)
eqv. with hit rate, recall, sensitivity
TPR = TP / P = TP / (TP + FN)
false positive rate (FPR)
eqv. with false alarm rate, fall-out
FPR = FP / N = FP / (FP + TN)
accuracy (ACC)
ACC = (TP + TN) / (P + N)
specificity (SPC)
SPC = TN / (FP + TN) = 1 ? FPR
positive predictive value (PPV)
eqv. with precision
PPV = TP / (TP + FP)

Here is a good java enabled page to calculate the ROC Curve.

And in case any one asks, ROC stands for Receiver Operating Characteristic. ……

Fast R Graphics

So you don’t know R  because you were always working on office projects and did not have time to learn. The R list looked down on you and told you to read the documentation first. And then you needed to create some fast R graphics and some R code.

Help is here-

Download R from,install it

open it-go to packages> set CRAN Mirror > to your country from drop down

type following in the R GUI near the ‘ >’ prompt-

“install.packages(“rattle”, dependencies=TRUE)”

so it should loook like

>install.packages(“rattle”, dependencies=TRUE)

Wait 15 minutes while downloads happen

Then packages>load package>rattle

Type rattle() at the command prompt

Now – in the new window called Rattle

load data from a .csv file using the browse options

click execute

Go straight to Explore-and click on distibutions.

Note you can also download rattle from , these guys are the best.

Here are the graphs


But what about the code (note some variable names disguised).The code may be intimidating to a novice R user but it is auto generated , its like jumping straight to SAS Enterprise without learning SAS Editor-

Go to the last tab -log and

see the auto generated code.


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