Course code STAT340

STAT340 Applied Methods in Statistics

Norsk emneinformasjon

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Lars Gustav Snipen
Teachers: Kathrine Frey Frøslie
ECTS credits: 10
Faculty: Faculty of Chemistry, Biotechnology and Food Science
Teaching language: EN, NO
(NO=norsk, EN=Engelsk)
Limits of class size:
Teaching exam periods:
The course starts in the spring semester. Teaching and exam is also in the spring semester.
Course frequency: Anually
First time: Study year 2015-2016
Preferential right:
Course contents:

Practical data analysis in R using various statistical methods followed by theoretical and practical interpretation of the results. The methods taught will be a collection from the following list: 

  • Multiple regression and regularization
  • ANOVA,
  • generalized linear models (logistic, poisson),
  • classification (lda, qda),
  • clustering,
  • principal component analysis (PCA),
  • mixed models, variance components
Learning outcome:


The students should know the assumptions, applications and theoretical background for the methods presented.


The students should master the use of R/ R Studio as a tool for practical data analysis. It will be emphasised that the students, to a given problem, should be able to formulate the problem in such a way that it can be analysed by means of suitable methods.

General competence

The students are able to decide which method(s) to use to model and analyse the problem, and to do the analysis. The students are also able to give the practical interpretation of and to assess the validity of models, methods and results.

Learning activities:
Lectures.Group work.Individual exercises.At least one compulsory assignment.Individual study.

We will use a "flipped classroom" type of teaching

Teaching support:

Lectures, colloquia and exercises. In addition, the teacher offers academic guidance during regular office hours.

Canvas and digital meetings (zoom)

Will be announced on the course pages in Canvas.

Students are required to:

Be able to perform descriptive statistics and draw conclusions from this.Know basic concepts and principles in probability theory with emphasis on stochastic variables and their properties.Be familiar with some common probability distributions,including the normal distribution, binomial distribution, Student’s t-distribution, F-distribution and the chi-square distribution.Understand basic estimation theory, including what is meant by confidence intervals, point estimates,expectation accuracy and standard error for an estimator.Reformulate simple situation descriptions and problem(s) to a relevant statistical model, and interpret the parameters in this.This applies to situations that can be covered by simple linear regression models, one-way analysis of variance or bivariate analysis.Test relevant cases using formal hypotheses,including setting up relevant hypotheses, testing these and interpreting the result. Basic skills in R/ R Studio.

This is covered by STAT100 or a similar course.

Recommended prerequisites:
STAT200, STAT210, STIN300 or equivalent.
Mandatory activity:
There will be at least one compulsory assignment.
Written exam, 3.5 hours, counts 100 %.
Nominal workload:
Lectures:  26 hours. Colloquia 26 hours. Exercises 26 hours. Individual study 172hours.
Entrance requirements:
Special requirements in Science
Type of course:
2 hours lectures/discussions of lecture videos per week. Six hours per week exercises/colloquia discussions.
Students are required to have a personal laptop.
An external examiner approves the examination questions and assesses 25 randomly selected examination papers.
Allowed examination aids: C2 All types of calculators, all other written aids
Examination details: One written exam: Letter grades