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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.

About this course

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.

  • Learning activities
    1. Lectures.
    2. Group work.
    3. Individual exercises.
    4. At least one compulsory assignment.
    5. Individual study.
  • Teaching support

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

    Canvas and digital meetings (zoom)

  • Prerequisites

    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.
  • Assessment method
    Written exam, 3.5 hours, counts 100 %.

    One written exam Grading: Letter grades Permitted aids: C1 All types of calculators, other aids as specified
  • Examiner scheme
    Two internal examiners, one of whom is the course supervisor, will ensure the quality of the exam papers. The exam answers will be assessed by the course supervisor as an internal examiner.
  • Mandatory activity
    There will be at least one compulsory assignment.
  • Notes
    Students are required to have a personal laptop.
  • Teaching hours
    2 hours lectures/discussions of lecture videos per week. Six hours per week exercises/colloquia discussions.
  • Preferential right
    M-BIAS
  • Admission requirements
    Special requirements in Science