PSPP

PSPP is a powerful yet lightweight software tool designed to perform advanced statistical analysis functions over small or very big amounts of sample data.

This program was conceived as a free alternative to the popular SPSS Statistics tool published by IBM, which is probably quite expensive. PSPP offers most of the core SPSS' functions, and it is available for free to students, engineers, statesmen, researchers and all kinds of professionals and users in general.

You might think that, given that PSPP is a free-to-use tool, it surely presents many limitations to its usage, however, this is not true at all. For starters, the program allows you to work with very large amounts of sample data, being you able to input more than a billion cases with more than a billion variables each. Then you can organize these sample data by getting it automatically sorted or transposed, for example. Next, you can start analyzing your data and getting results by applying functions like linear or binary logistic regressions, univariate analysis, bivariate correlations, factor analysis, binomial statistics, Chi-Square, Reliability, means comparison, cluster analysis, T-tests and many others, as well as generating histogram, scatterplot and barchart graphs. All the results are presented to you on a separate window called the "Output Viewer".

PSPP offers a simple and intuitive user interface which also looks pretty similar to SPSS'. You can easily import your sample data from third-party programs such as spreadsheets, text files or database files, and also, you can import two or more sample data sets and perform operations between them, such as comparing or concatenating them. Your results can be exported to such popular formats as PDF, text files or HTML as well.

In conclusion, PSPP is an excellent alternative to SPSS or any other statistical analysis tool, with the plus that you can use it without worrying about expensive licenses or heavyweight installers. Of course, using this program demands a very good level of knowledge in Statistics and the functions you plan to use or apply, as otherwise you might get the wrong results.