Statistical analyses and visualization in R: II, 15 credits
(Statistisk analys och visualisering i R: II, 15 högskolepoäng)
Course code | 1041MA |
---|---|
Subject area | Mathematical Statistics |
Main field of study | No main subject |
Progressive specialisation | G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements) |
Academic school | School of Natural Sciences, Technology and Environmental Studies |
Disciplary domain |
Natural sciences 100%
|
Grading scale | AF |
Education cycle | First-cycle (Bachelor) |
Course level | A |
Language of instruction | English |
Valid from | Autumn semester 2019 |
Validation
This course syllabus was validated by the Management Board of the School of Natural Sciences, Technology and Environmental Studies at Södertörn University on 2019-01-30 according to the stipulations in the Higher Education Ordinance.
Entry requirements
Statistical analyses and visualization in R: I, 15 credits and English 6 or the equivalent.
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
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.
1001, Statistical analyses and visualization in R: II, Practicals, 10 credits
(Statistisk analys och visualisering i R: II, övningar, 10 högskolepoäng)
Grades permitted: AF1002, Statistical analyses and visualization in R: II, Project, 5 credits
(Statistisk analys och visualisering i R: II, Projektarbete, 5 högskolepoäng)
Grades permitted: AFCourse 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.
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.
If a student has a certificate from Södertörn University for compensatory support, the examiner has the right to decide on an adapted examination or alternative form of examination in accordance with Södertörn University's regulations.
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.