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Programming in statistics, 7.5 Credits

Swedish name: Statistisk programmering

This syllabus is valid: 2017-01-02 and until further notice

Course code: 2ST035

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Statistics: Second cycle, has only first-cycle course/s as entry requirements

Grading scale: VG Pass with distinction, G Pass, U Fail

Responsible department: Department of Statistics

Revised by: Rector of Umeå School of Business and Economics, 2016-11-03


In statistical work there is often a demand for specific analyses that cannot be done using menu-based statistical software. In such situations, programming skills are good to have. The purpose of the course is to provide an introduction to such statistical programming.

The course is based on the statistical work and programming environment R, which is an implementation of the programming language S. This environment is well on its way to become a de facto standard for professional statisticians. The program is freely available under a GPL license and the student learns how to download and install the program. The first part of the course gives an introduction to R. The student learns how to import and export data, to handle different datatypes, create and use scripts, store data and results, create and save graphical illustrations, and also how data can be manipulated in R with logical operators.

Furthermore, the course provides an overview of the implemented analytical methods, such as methods for hypothesis testing, ANOVA and linear regression. The latter part of the course deals with functions, optimization of code, implementation of C code, and how to create your own R packages.

Expected learning outcomes

After completed course the student should be able to:
  • create simple programs in the programming language R using basic programming techniques such as scanning and printing data, allocation and manipulation of data structures, self-written functions, repetition and conditional statements
  • carry out simulation studies and analyze and evaluate the resulting performance
  • illustrate the statistical models and the results of statistical surveys graphically.

Required Knowledge

Univ: At least 90 credits including at least 60 credits in statistics and/or mathematical statistics, or equivalent. Proficiency in English equivalent to Swedish upper secondary course English B (English/6).

Form of instruction

The course consists of lectures, seminars and tutorials. Much of the teaching is of laboratory character and is in the form of computer lectures. Mandatory written assignments and seminars are included.


Examination modes

The examination consists of written and oral presentations of given assignments. Reports of assignments should be handed in or presented at predetermined dates. To pass the course requires that all seminars and assignments are satisfactorily reported and approved. The grades used are: VG (Pass with distinction), G (Pass), and U (Fail).
The gr­ade is a com­prehensive eva­luation of the results of the various parts of the examinations and is not granted until all mandatory tasks have been passed.
A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional test will be held according to a pre-determined schedule.
After two failed examinations on the course, the student has the right to request another grading teacher. Writ­ten requests should be handed to the Director of Studies no later than two weeks before the date of the next exam­ination.
Examinations based on the same course syllabus as the ordinary examinations are guaran­teed to be offered up to two years after the date of the student's first registration for the course.

Academic credit transfer

Academic credit transfers are according to the University credit transfer regulations.


Valid from: 2018 week 3

A first course in statistical programming with R
Braun John, Murdoch Duncan James
Cambridge, N.Y. : Cambridge University Press : 2007 : 163 s. :
ISBN: 978-0-521-87265-2 (inb.)
Search Album, the University Library catalogue

Venables W. N., Smith D. M., the R Core Team
An Introduction to R
R Core Team :

S programming
Venables W. N.q (William N.), Ripley Brian D.
New York : Springer : cop. 2000 : x, 264 s. :
ISBN: 0-387-98966-8 (alk. paper)
Search Album, the University Library catalogue

Chambers John M.
Software for data analysis : programming with R
New York, N.Y. : Springer : cop. 2008. : 498 p. :
ISBN: 978-0-387-75935-7 (hbk.)
Search Album, the University Library catalogue