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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?

EDIT
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.[1]
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 RapidMiner and 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.

[1]: Gnumeric, SciPy, Scilab, GNU Octave and alike seem to me less directed to social sciences.

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    $\begingroup$ @Matthias: I think if your students are coming/aiming from/for a social sciences field, teaching them R as a first step in Statistics is an overkill. Most of them will have problems with the concept of console, commands, syntax etc. and you'll spend more time going through "programming concepts" (what is 'function', 'loop' etc.) than "statistics". I base this on prior experience, when I did tutorials for a Stats 101 in a Soc.Science department; people missed the point of the lectures cause they focused more in getting R to work for them than actually exploring their data. $\endgroup$ – usεr11852 says Reinstate Monic Apr 10 '13 at 15:57
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    $\begingroup$ @user11852: You may be right but it's sad that high schools send students out with no programming exposure at all. Or universities that allow this gap. There ought to be no student coming to a university class that doesn't know what a loop or function is. Delaying exposure just pushes the problem elsewhere. $\endgroup$ – curious_cat Apr 11 '13 at 19:04
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    $\begingroup$ @user11852: The other less palatable option might be for Satistics departments to insist that "Stats 101 for Social Sciences" classes have a prerequisite of some programming experience or a remedial class on programming. These days when almost all subjects are so heavily computation biased there really is no reason why Programming-101 shouldn't be the very first class everyone takes. $\endgroup$ – curious_cat Apr 11 '13 at 19:51
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    $\begingroup$ For what it's worth, I have used R successfully in my introductory stats course for political science. I used RStudio. I also had weekly "labs" where I would allow students to work together on small assignments, while I went around and answered questions. With some well-commented example code, the students did well and hardly complained at all. They actually complained a lot less about R than they did the previous semester when I used Stata. Because Stata isn't free, students had to come in for lab hours to do their work -- they hated it. $\endgroup$ – Jason Morgan Apr 30 '13 at 21:22
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    $\begingroup$ I think that R in conjunction with RStudio can be an excellent approach. It also sets the stage for reproducible research practice, unlike menu systems. I would recommend giving out several code templates that the students can load into RStudio from the web (RStudio makes this easy) and let the students do the work of changing variable names and statistical models to what is needed for the problem at hand. $\endgroup$ – Frank Harrell May 3 '13 at 18:46
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Maybe Gretl? http://gretl.sourceforge.net/

It is free and used at our University for undergraduate statistics.

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We're looking for long answers that provide some explanation and context. Don't just give a one-line answer; explain why your answer is right, ideally with citations. Answers that don't include explanations may be removed.

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    $\begingroup$ +1. Excellent suggestion. I have always found Gretl's GUI intuitive and to the point and the feedback it provides accurate and without too much frills that would put off some less "techy" students. Plus it is free, well-documented and has an R console if someone if incline to see something a bit "deeper". $\endgroup$ – usεr11852 says Reinstate Monic Apr 10 '13 at 15:59
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I would avoid most of the "famous" stuff, MatLab, Maple, Mathematica, JMP, SAS, or Minitab, because when your students graduate they have to pay thousands of dollars per year to use it professionally. Each company tends to have its particular favorite tool, and if you teach them a tool that their company won't pay for then their skill-set is wasted. I also don't like the proprietary libraries - they train users to push buttons and if the user wants to go somewhere else (JMP or whatever) there is no carry-over of learning.

Python inclding SciPy/NumPy is pretty good. It is open source and well supported. It has a learnable/easy grammar. It is still interpreted so its not screaming fast, but if they don't know any scripting or spreadsheeting then it is much faster than they would ever need. PythonXY is good version, has good libs and support. I also like that GUI programming is possible through it. Building standalone applications in windows is a little challenging but likely waaaay above the level of your students. (edit) Sage and Cython substantially improve the value proposition of Python. The interface, and usability are substantially improved. A compiled code that is 1000x faster than a pretty good interpreted code sounds great (or amazing) to me. EDIT: I have had some fun using the Anaconda (aka conda) distributions, and they are also very straightforward to use.

I am not a huge fan of Perl. It is a little outdated. It is about parsing and processing text more than math/science. Don't get me wrong, it can do math/science, but if you know VBA then MSWord can do math/science. Being able to isn't the same has having a particular job as your primary focus.

I like R, even though you don't, because it is aggressively being developed by qualified PhD's in math/stats. This means that even though the grammar might be klugy, it is going to have libraries that are up to date, and proven error free. (In general)

Excel is not a bad start. Once you know one spreadsheet it makes using any other easier. In a business setting nearly every company has MicroSloth office so Excel isn't a bad idea. I don't like their scripting, but that is just preference, I can still use it. It costs about 150 dollars US compared to 5000 dollars US for some of the other softwares so its entry-cost for normal folks is more reasonable.

JMP script language is alien. It does not translate to other (nonSAS) software. Stay away from it. The only redeemable feature of the language is that it can (in some limited sense) run "R" code. If you are coding in "R" just use "R" and "RStudio".

I have not used MathCAD so I cannot speak to its relevance. I think it is more symbolic, less about importing external data. It is cheaper, so far. It is not free and open. Facility at it doesn't translate to facility in another language. (EDIT) Also in this category is EES, which I am similarly not impressed with outside of a very narrow window of use.

EDIT: I have been impressed a little by LabVIEW. It is simple enough to use that a few hours can get someone capable. It runs really fast, like literally 1000x faster than MatLab for literally the exact same (MathScript) code. If you have some heavy-lifting, it is worth a little consideration. It does cost money, but something in the neighborhood of 1/5 of conventional big-iron.

Best of luck

EDIT: I would not use Statistical LAboratory because even when you select "english" for language it comes out in German, and it does not uninstall on windows 7. Both administrative weaknesses make it a no-go for me. I can't operate it, and when I tried to remove it that failed.

By trial and error I discovered the menu setting to make it display in English. It appears to be a relatively simple (and therefore useful and a consistent) interface into some R libraries for data processing and display. I will have to look more into it, so at this point 'the jury is still out.'

EDIT more:

->Here<- is a fun link to a whole other discussion about tools and workbenches.

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    $\begingroup$ There's also RPy rpy.sourceforge.net, R as a library for Python, so you get the up-to-date, proven-error-free aspects of R with the syntactic simplicity of Python. $\endgroup$ – Ghillie Dhu Apr 10 '13 at 20:09
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    $\begingroup$ "they train users to push buttons and if the user wants to go somewhere else (JMP or whatever) there is no carry-over of learning." SAS, a proprietary program, doesn't particularly train well for "pushing buttons", and having trouble carrying over between different languages is hardly a feature of proprietary software alone. Heck, I was more at home going from SysStat to JMP than I was from Python to R. $\endgroup$ – Fomite May 18 '13 at 5:16
  • $\begingroup$ @Epigrad - I watch it turn engineers brains off all the time. Dozens and dozens of folks. I am glad that you found utility for it, but I strongly expect that you are an outlier and the general trend of harm is not substantially changed by your experience. $\endgroup$ – EngrStudent - Reinstate Monica May 18 '13 at 22:40
  • $\begingroup$ @EngrStudent: Thank you for your effort to try Statistical Laboratory! The menu setting for English language is indeed non-intuitive, but after having it set once I have not encountered more issue with language. Unfortunately, I cannot get the "R-Graph Wizard" to work, though the normal R-Graph works fine if I put in some R code. Therefore, I will give my students some example code snippets to produce basic graphics. Maybe I switch earlier to RStudio... $\endgroup$ – non-numeric_argument May 21 '13 at 15:51
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You could try using Gnumeric, a highly thought of spreadsheet, there is also an Open Office spreadsheet. Provided you explain the pitfalls of using spreadsheets, particularly Excel, after college in their subsequent practical lives they may not have the luxury of something like SPSS, but could still get useful service from these free products that are not too demanding of maths and programming skills. Many office environments contain Excel by default.

Have a look at:

http://groups.google.com/group/sci.stat.math/browse_thread/thread/26fe9a9a0d91139d# - Statistics and Excel 2007

and search for similar references such as

http://groups.google.com/group/comp.soft-sys.stat.spss/browse_frm/thread/3940bcd6c6266f1b/d85edd4978e53568?hl=en#d85edd4978e53568 Keeling, Kellie B. & Pavur, Robert J. (2007). A comparative study of the reliability of nine statistical software packages. Computational Statistics & Data Analysis, 51, 3811–3831.

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I have been CalEst. The license is cheap, like 10 buck and provides both calculations/graphics as well as great simulation/activities for the students to practice. Moreover, in their website, they have some tools, mainly on distributions you may find useful.

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  • $\begingroup$ This answer is a bit short. Could you some more about why you will propose this software, and which afdvantages it have compared with the competition? $\endgroup$ – kjetil b halvorsen Dec 1 '17 at 19:16
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We have been started using Rguroo. This software is newly released. It is R based, but no knowledge of R coding is required. It’s also a web application so you simply login on a browser. My students can save their work at any stage and go back to complete their work. The graphical user interface is very intuitive and the outputs look great.

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We have been using the beta version of Rguroo in our introductory statistics courses at California State University, Fullerton for the past three years. They have now (August 2019) have released an official version, see https://Rguroo.com. This is a web-application Statistics software that works in any browser. This software is designed for teaching and they offer one on one demo and training for faculty; just email or call them to arrange for a demo. The software runs R in the background, but you don’t need to know R, it’s all point and click. It has many great features, including detailed outputs, great graphic tools, probability calculator, and simulation tools. I specially like The reproducibility features where you can save your work at any stage and come back and continue where you left off. You can also share your work with students through what they call RGR files. Bottom line ... it’s a great and we continue to use it.

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I personally use DataMelt software for teaching of statistics. It's very well documented, it has tutorials, books and a lot of examples to look at. What is also important is that one can search for any example, and you can get a reasonable answer (in Javadoc and code snippets). Students can learn not only Python (which is the default programming language), but also how to code statistical methods in Java. In my view, this is a significant strength: students do not need to learn very specialized "statistical" language, like R-stat. They can learn Java at the same time too, which can open a lot of opportunities if they will decide to go to the industry.

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There is a new software called Rguroo which is a web application. It is very convenient to use, as it does not require downloads or installation. Rguroo has an R engine, but its use does not require R coding as it enables you to use the power of R using point-and-click graphical interface. Every analysis is savable and reproducible. We have been using this software for our introductory and intermediate statistics courses in the past three years. At this point it is free and you can create an account at www.Rguroo.com. Based on the information that I have it will remain free for all faculty and it will have a reasonable annual subscription charge, somewhere between 10 to 20 dollars, for students.

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