Translation of the Swedish course syllabus 
 
Statistical analyses and visualisation in R: II, 15 credits
(Statistisk analys och visualisering i R: II, 15 högskolepoäng)
 
Course code: 1041MA
Subject area: Mathematic Statistics
Main field of study: No main subject
Academic school: School of Natural Sciences, Technology and Environmental Studies
Disciplinary domain: Natural Sciences 100%
Grading scale: AF
Education cycle: First-cycle (Bachelor)
Course level: A (introductory)
Progressive specialisation: G1F (First cycle. Entry requirements: 0-60 credits from first-cycle courses)
Language of instruction: English
Valid from: HT 2019 (autumn semester)


1. Validation

This course syllabus was validated by the Faculty Board for Natural Sciences, Technology and Environmental Studies at Södertörn University on 2019-01-30 according to the stipulations in the Higher Education Ordinance.

2. Entry requirements

Statistical analyses and visualisation in R: I, 15 credits

3. Learning outcomes

Upon completion of the course, the student is able to:
  • explain and account for least square and maximum likelihood estimation of statistical parameters
  • design studies and experiments to optimise the potential for statistical examination of research questions
  • describe the importance of the residuals' distribution for the choice of statistical method
  • describe the function of general linear models, and analyse statistical models using other distribution functions
  • describe basic and complex Bayesian statistical models
  • describe the design of complex statistical models with fixed and random variables
  • perform basic and complex statistical models in R
  • perform basic and complex visualisations of statistical analyses in R

  • 4. Course content, modules and examinations

    This course deepens the understanding of statistical methods, focusing on statistical models for analysing the results of studies and scientific experiments. We develop skills in working with R, RStudio and RMarkdown. The course introduces generalised linear models for handling data with different correlative structures, and response data fitted to different distribution functions. Students are taught to use models with both fixed and random variables, mixed models, and models with different types of residual distribution functions. Students also learn how to use generalised additive models, and multivariate statistical methods. Classical frequentisitc inference methods are contrasted with Bayesian analysis of statistical models. Students learn the advanced visualisation of results from statistical models using various plotting functions in different R packages.

    • Statistical analyses and visualisation in R: II, 15 credits
      (Statistisk analys och visualisering i R: II, 15 högskolepoäng)


    • 1001, Statistical analyses and visualization in R: II, Practicals, 10 credits
      (Statistisk analys och visualisering i R: II, övningar, 10 högskolepoäng)
      Grades permitted: A/B/C/D/E/F

      1002, (Statistical analyses and visualization in R: II, Project, 5 credits
      (Statistisk analys och visualisering i R: II, Projektarbete, 5 högskolepoäng)
      Grades permitted: A/B/C/D/E/F

    5. Course design

    If the course is a distance course: All teaching is in English in the form of written instructions and demonstrations, as well as video lectures.

    If the course is taught on campus: All teaching is in English in the form of written instructions and demonstrations, as well as seminars, lectures and video lectures.

    6. Examination format

    1001, Statistical analyses and visualisation in R: II, Practicals, 10 credits
    (Statistisk analys och visualisering i R: II, övningar, 10 högskolepoäng)
    Completed exercises with individual written reports

    1002, Statistical analyses and visualisation in R: II, Project, 5 credits
    (Statistisk analys och visualisering i R: II, projektarbete, 5 högskolepoäng)
    Individual written report

    All examinations can be written in English or Swedish.
     The grading criteria are distributed prior to the start of a course or module.

    7. Reading list


    8. Restrictions on accreditation

    The course may not be accredited as part of a degree if the contents are partly or wholly the same as a course previously taken in Sweden or elsewhere.