I am looking for a statistical software package which I can use in an introductory course of statistics for a social science study programme. The students have no prior knowledge of statistics and no experience with programming languages either. The goal is to introduce them to basic statistical concepts (as means, variance, sum of squares, p-values, ... and finally linear regression) and to enable them to conduct basic analyses on their own using example datasets. The course should be about learning concepts by doing statistics rather than memorizing formulas (although I think formulas are important).
Therefore, I am looking for an alternative to the usual syntax (as normal R) or point-and-click (as SPSS or Rcmdr) driven software. The software should be easily learnable and it should have a clear graphical user interface which visualizes datasets and offers the standard graphs and tables. The best would be if it visualized all different steps of an analysis (e.g. reading & manipulating data, computation of descriptive measures, making descriptive tables & graphs, computation of inferential measures, plotting of inferential graphs, export to a report).
Do you have suggestions of (open-source or free) statistical software which is suited for learning and first practising statistics?
Thanks for your suggestions. I have looked into gretl, and two other programms I have found during my own online enquiry: RapidMiner and Statistical Lab.
I have found that
gretl's interface and output is more clear and focused than e.g. Rcmdr, SPSS or Stata. Therefore, it is a well qualified tool for starting teaching statistics from my point of view.
However, the flowchart GUI's of
Statistical Lab impressed me as they visualize the single steps of a statistical analysis (starting with loading data). I think this might be helpful to many students who struggle with the usual focus on mathematical explanations. Of course, RapidMiner seems to me too overloaded with functions, menus and buttons for beginners whereas the Statistical Lab is much more focused. The big plus of the Statistical Lab is the console-like "R-Calculator" with a "R-code Wizard" which assists in producing real R syntax as the Statistical Lab relies on
R for its computations.
Finally, I decided to start with the Statistical Lab in the first semester while introducing the basic concepts and switch to RStudio (and Rcmdr) in the second semester.
: Gnumeric, SciPy, Scilab, GNU Octave and alike seem to me less directed to social sciences.