Problems viewing this page? Go to simple view

Menu

Welcome at

 

Statistics Worldwide

 

 

Statistical software

 

Choosing appropriate statistical software depends on the type of analysis, computer platform being used for the analysis, technical level of the researcher using the statistical package and the cost of the statistical package. In this section free and commercial software are discussed. There are many more solutions. If you feel important software is missing contact us via team@StatisticsWorldwide.com.

 

Background photo: NASA's Marshall Space Flight Center license

Free statistical software

 

 

G7 from Inforum

 

G7 is an econometric regression and model-building program for Windows. It is designed for estimation of regression equations with annual, quarterly, or monthly data. G7 takes its name from Carl Friedrich Gauss, the originator of the method of least squares.

 

To the G7 website

 

GGobi

 

GGobi is used for the graphical exploration of data. It allows the user to have multiple displays on the screen at once. These displays include barcharts, parallel coordinates plots, scatterplots (1D, 2D, 3D) and tours. Tours are animated scatterplots that change as various projections are made using the other variables. When an observation is highlighted in one display, it would be highlighted in every other display. GGobi allows users to take a ‘brush and spin’ graphical approach to cluster analysis.

 

The project initially arose from an interests in finding ways to draw pictures of high-dimensional data. It has evolved to handle other types of data, including missing values and network/graph data.

 

The name GGobi arose with an earlier version of the software, XGobi. Originally XGobi was called Xdataviewer, as it had evolved from an older software called Dataviewer, written in Lisp. It was initially developed to look at data matrices. Dataviewer was started in the mid-80s, XGobi started in 1988/9, and GGobi started in about 1999.

 

To the GGobi website

 

Gretl

 

Gretl is a time series modelling program. It comes with point and click windows for stationarity tests, allowing the user to choose the type of test and whether to take into account the constant and/or time trend(s). The forecasts are made automatically and include actual values, point estimates, prediction intervals and a graph. This can be very handy when differencing or autocorrelation is involved. Gretl is very similar to EViews (commercial Windows-based econometric software).

 

Econometric code of Ramu Ramanathan, Professor Emeritus of the University of California, San Diego, was the starting point for the development of gretl. It is (mostly) written in the C programming language.

 

To the Gretl website

 

Instat

 

Instat is a general statistical package with a long history. Non commercial use on 1 PC is free. It is widely used in the UK especially for the analysis of climatic data. The windows version that was developed at the University of Reading has also been used in many countries on statistics courses and on courses related to health, agriculture and climatology. According to the makers Instat provides a relatively gentle introduction to using statistical software packages. So if you wish to consider adding a statistical package to your software repertoire Instat is good starter.

 

To the Instat website

 

R

 

R is a general purpose command driven statistical software. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is growing in popularity due to being free, (relatively) easy to use, having many features and being customisable (people can write their own R programs). R is very similar to SPlus. One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

 

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.

 

R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. The current R is the result of a collaborative effort with contributions from all over the world. R was initially written by Robert Gentleman and Ross Ihaka also known as "R & R" of the Statistics Department of the University of Auckland. Since mid-1997 there has been a core group with write access to the R source.

 

To the R website

 

WinBugs

 

The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. It uses the software philosophy of component-oriented progamming, which means the program is constructed from a set of cooperating components. This set is not closed, and so the functionality of WinBUGS can be continuously extended by developing new components.

 

The BUGS Project are a team of UK researchers at the MRC Biostatistics  Unit, Cambridge, and Imperial College School of Medicine, London. The project began in 1989 and there are now a number of versions of BUGS. One of the most useful new features in WinBUGS 1.4 is scripting - the ability to run WinBUGS programs without having to point and click your way through the commands. Using this feature, it is possible to call WinBUGS from within other programs like R, Stata and SAS. Several users have written code to do this.

 

To the WinBugs website

 

Commercial statistical

 

software

 

 

SAS

 

SAS (pronounced "sass") once stood for "statistical analysis system," and was originally created by Jim Goodnight and North Carolina State University colleagues to analyze agricultural-research data. Jim Goodnight holds a doctorate in statistics. With its unique business model (software licensed annually) and solid reputation for innovation SAS is among the world's largest privately owned software companies. With SAS - using advanced analytics - you can turn data into meaningful information.Jim Goodnight

 

SAS was founded in 1976 to help all sorts of customers. Jim Goodnight (on the picture) is CEO of SAS since the start. The company has overseen an unbroken chain of revenue growth unheard of in the software industry. The software can run across all platforms, using the multivendor architecture for which it is known today. SAS runs on IBM mainframes, Unix, Linux, OpenVMS Alpha, and Microsoft Windows and is used at more than 65,000 sites in over 135 countries.

 

SAS is driven by SAS programs, which define a sequence of operations to be performed on data stored as tables. Although non-programmer graphical user interfaces to SAS exist these GUIs are most often merely a front-end that automates or facilitates the generation of SAS programs. The functionalities of SAS components are intended to be accessed via application programming interfaces, in the form of statements and procedures. Compared to general-purpose programming languages, this structure allows the user/programmer to concentrate less on the technical details of the data and how it is stored, and more on the information contained in the data. This blurs the line between user and programmer, appealing to individuals who fall more into the 'business' or 'research' area and less in the 'information technology' area, since SAS does not enforce (although it recommends) a structured, centralized approach to data and infrastructure management.

 

SAS is also renowned for its corporate culture, which has made it a fixture on best workplaces lists. In 2012, SAS ranked No. 1 on the elite World's Best Multinational Workplaces list from Great Place to Work. Jim Goodnight has also been an active speaker and participant at the World Economic Forum. He was also named one of America's 25 Most Fascinating Entrepreneurs by Inc. magazine. SAS' corporate headquarters in North Carolina has a distinctly academic feel, nestled on 300 wooded acres that employees call the "campus".

 

(Source: Wikipedia and SAS)

 

Youtube SAS-channel

Contact SAS

 

 

SPSS

 

SPSS The many features of SPSS Statistics are accessible via pull-down menus or can be programmed with a proprietary 4GL command syntax language. Some complex applications can only be programmed in syntax and are not accessible through the menu structure. The pull-down menu interface also generates command syntax; this can be displayed in the output, although the default settings have to be changed to make the syntax visible to the user. Programs can be run interactively or unattended, using the supplied Production Job Facility. Additionally a "macro" language can be used to write command language subroutines and a Python programmability extension - introduced in SPSS 14 - can access the information in the data dictionary and data and dynamically build command syntax programs. The Python extension also allows SPSS to run any of the statistics in the free software package R or a VB.NET program using supplied "plug-ins".

 

SPSS Statistics places constraints on internal file structure, data types, data processing, and matching files, which together considerably simplify programming. SPSS datasets have a two-dimensional table structure where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age, sex, or household income). Only two data types are defined: numeric and text (or "string"). All data processing occurs sequentially case-by-case through the file. Files can be matched one-to-one and one-to-many, but not many-to-many.

 

The graphical user interface has two views which can be toggled by clicking on one of the two tabs in the bottom left of the SPSS Statistics window. The 'Data View' shows a spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the data cells can only contain numbers or text and formulas cannot be stored in these cells. The 'Variable View' displays the metadata dictionary where each row represents a variable and shows the variable name, variable label, value label(s), print width, measurement type and a variety of other characteristics. Cells in both views can be manually edited, defining the file structure and allowing data entry without using command syntax. This may be sufficient for small datasets. Larger datasets such as statistical surveys are more often created in data entry software, or entered during computer-assisted personal interviewing, by scanning and using optical character recognition and optical mark recognition software, or by direct capture from online questionnaires. These datasets are then read into SPSS.

 

SPSS Statistics can read and write data from ASCII text files (including hierarchical files), other statistics packages, spreadsheets and databases. SPSS Statistics can read and write to external relational database tables via ODBC and SQL.

 

Statistical output is to a proprietary file format (*.spv file, supporting pivot tables) for which, in addition to the in-package viewer, a stand-alone reader can be downloaded. The proprietary output can be exported to text or Microsoft Word, PDF, Excel, and other formats. Alternatively, output can be captured as data (using the OMS command), as text, tab-delimited text, PDF, XLS, HTML, XML, SPSS dataset or a variety of graphic image formats (JPEG, PNG, BMP and EMF).

 

History

The Statistical Package for the Social Sciences (SPSS) had its origin in 1967 in the frustration of Norman H. Nie (in the picture), a then 22-year-old PH.D. candidate political science at Stanford University, while trying to use a computer application created for biologists to analyze data describing the political culture of five nations. Nie enlisted the help of fellow doctoral candidate Dale H. Bent with a background in operations research and Hadlai Hull who had recently received his MBA from Stanford and by 1968 SPSS was born. Nie and Hull left Stanford to pursue careers at the University of Chicago. Though they brought their SPSS program along with them both concentrated on their academic careers. SPSS grew by itself thanks to the fact that it was well documented. The application became so increasingly popular that it called into question the University of Chicago's status as a tax-exempt organization. Mainly for that reason, Nie and Hull incorporated their operation in 1975, and SPSS officially became an independent company. Dale Bent left the scene and accepted an academic position at the University of Alberta in his native Canada instead of becoming involved with the new Chicago-based enterprise.Norman Nie

 

For several years, SPSS remained a part-time endeavor for Nie and Hull. While competitor SAS was making strides during the late 1970s by partnering with the likes of IBM, SPSS remained focused on its base of academic users. The company sought to keep its software easy to use for those who were not computer savvy, and even paid fellow academicians to make upgrades. SPSS eventually was adopted by government and business users as well. A graphic user interface (GUI) was developed based on drop-down menus rather than syntax. In 1980 Nie and Hull decided to take their enterprise to a new level. This led to many software developments - also triggered by acquiring other software companies - and strategical alliances in the eighties and nineties (read more at www.fundinguniverse.com). In May 1993, SPSS reincorporated in Delaware under the same name and made an initial public offering of stock on the NASDAQ exchange. Also during this time, the company made an acquisition (of SYSTAT Inc.), and released software compatible with Microsoft Windows 95. In 2002, sales reached $209.3 million and employees numbered 1,263.

 

In 2003, SPSS introduced Predictive Web Analytics, a new product that made it possible for users to see patterns in Web data and design more effective, relevant Web sites. SPSS had its eye on audio and video mining as a new niche. The company had several products in the pipeline when it faced financial and legal challenges. While sales remained good, earnings declined. Allegations were that the company had deceived stockholders by issuing misleading financial information. Though this caused SPSS stock prices to slip management remained optimistic about restoring earnings and stockholder confidence to prior levels, given, in particular, new leadership in the sales department and new markets for predictive analytics.

 

Norman Nie served as CEO until 1991 and continued as Chairman of the Board and software design consultant until 2008. SPSS was acquired by IBM in October, 2009.  As a result, the organisation’s name changed immediately to “SPSS, an IBM company"  The package name changed from SPSS to PASW (Predictive Analytic SoftWare) and from release 18 onwards as IBM SPSS Statistics (in which SPSS now stands for Statistical Product and Service Solutions) but most users still call it SPSS. IBM SPSS is now fully integrated into the IBM Corporation, and is one of the brands under IBM Software Group's Business Analytics Portfolio, together with IBM Algorithmics, IBM Cognos and IBM OpenPages.

 

(sources: Wikipedia and www.fundinguniverse.com)

 

To the IBM website