Stata is a full-featured statistical programming language for data analysis. Stata is available in several packages. It is also packaged for Business, Academic or Governmental institutions. Read more
Features and limits
Stata package: |
Small | Intercooled IC (the standard version) |
SE (an extended version) |
| Number of observations | 1,200 | unlimited | unlimited |
| Number of Variables | 99 | 2,047 | 32,767 |
| Number of characters in a command | 8,697 | 67,800 | 1,081,527 |
| Number of options for a command | 70 | 70 | 70 |
| Length of a string variable | 244 | 244 | 244 |
| Length of a variable name | 32 | 32 | 32 |
Comparison with other packages
R:
R is a free software package designed for use with command line only. While being a language is one of R's greatest strengths, it can make it harder to learn for those without programming experience. However, once learnt, you are no longer subject to price increases. The developer’s community ensures to constantly provide add-ons and also ensures that the software will continue to exist. R is extremely versatile in graphics, and generally good for people who really want to find out “what their data have to say”.
SAS:
SAS is the second most costly package. It can be used with, both, command line and graphical user interface (GUI). SAS is particularly strong on data management (especially with large files), and good for cutting edge research. It covers many graphical and statistical tasks. The main focus is on business customers now.
SPSS:
SPSS is the first choice for the occasional user. However, it is the most expensive of the four. SPSS is clearly designed for point-and-click usage on the GUI. A command structure exists, but it is not well defined and sometimes inconsistent. SPSS is good for basic data management and basic statistical analysis, but rather weak in graphics. In future, SPSS might be the weakest of the four packages with regard to the scope of
statistical procedures it offers due to its main focus on business customers.
Stata:
Stata is designed for the usage by command line, but it also offers a GUI that allows for working with menus. The simple and consistent command structure makes it rather easy to learn. It is the cheapest of the packages that entail costs, and it offers additional reductions for the educational sector. Stata is relatively weak on ANOVA, but extraordinary on regression analysis and complex survey designs. Stata is completely focused on scholars. In the future, Stata may have the strongest collection of advanced statistical procedures. You can get orders from http://www.stata.com/order/
Graphical Interface
Evolving from command-driven interface, stata also operates in a graphical windowed interface/Environment.
Results window
All outputs appear in this window. Only graphics will appear in a separate window.
Command window
This is the command line where commands are typed for execution.
Variables window
All variables in the currently open dataset appear here. By clicking on a variable, its name can be transferred to the command window.
Review window
Previously used commands are listed here and can be transferred to the command
window by clicking on them.
Major Buttons
The most important button functions are the following:
• Open (use): Opens a new data file.
• Save: Saves the current data file.
• Print results: Prints the content of the results window.
• New Viewer: Opens a new viewer window, e.g. to open log-files.
• New Do-file Editor: Opens a new instance of the do-file editor (same as doedit).
• Data Editor: Opens the data editor window (same as edit).
• Data Browser: Opens the data browser (same as browse).
• Break: Allows to cancel currently running calculations.
Menu
Almost all commands can be called from the menu. However, we do not recommend to learn Stata using the menu commands since the command line will give the user much better control and allows for a much faster and more exact working process.
External links
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