# STAT340 Applied Methods in Statistics

Credits (ECTS):10

Course responsible:Solve Sæbø

Campus / Online:Taught campus Ås

Teaching language:Engelsk, norsk

Limits of class size:250

Course frequency:Anually

Nominal workload:Lectures: 26 hours. Colloquia 26 hours. Exercises 26 hours. Individual study 172hours.

Teaching and exam period:The course starts in the spring semester. Teaching and exam is also in the spring semester.

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

Knowledge

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

Skills

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.

1. Lectures.
2. Group work.
3. Individual exercises.
4. At least one compulsory assignment.
5. Individual study.
• Lectures, colloquia and exercises. In addition, the teacher offers academic guidance during regular office hours.

Canvas and digital meetings (zoom)

• 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.

• Written exam, 3.5 hours, counts 100 %.

One written exam Grading: Letter grades Permitted aids: C1 All types of calculators, other aids as specified
• An external examiner approves the examination questions and assesses 25 randomly selected examination papers.
• There will be at least one compulsory assignment.
• Students are required to have a personal laptop.
• 2 hours lectures/discussions of lecture videos per week. Six hours per week exercises/colloquia discussions.
• M-BIAS
• Special requirements in Science