ST813: Statistical Modelling

Students taking the course are expected to:

• Have knowledge of linear algebra, calculus, basic statistics
• Be able to use the statistical software R 

The course builds on the knowledge acquired in the courses
ST521: Mathematical Statistics and on knowledge of linear algebra
corresponding to the course MM538: Algebra and Linear Algebra, and gives
an academic basis for advanced courses in statistics and master thesis
projects.

In relation to the competence profile of the degree it is the explicit focus of the course to:

• have
  an overview of the various types of linear and generalized linear
  models and the main examples of these, as well as to identify which
  problems can be solved by means of such models;
• be skilled at
  manipulating the mathematical and statistical elements of linear and
  generalized linear models and to clearly distinguish between exact and
  asymptotic results;
• know how to securely apply the theoretical
  results for linear and generalized linear models to concrete examples
  and explain the practical interpretation of the results
• have familiarity with the statistical package R, and routine in its use for statistical modeling.

The learning objective of the course is that the student demonstrates the ability to:

• recognize
  the different types of statistical models and describe their
  similarities and differences, and explain the role that the response
  variable, explanatory variables, variance function and link function
  play for statistical modeling;
• be able to manipulate the
  mathematical and statistical elements of linear and generalized linear
  models, such as parameters and principles of estimation, the derivation
  of statistical tests based on standard errors deviance and residual sum
  of squares;
• obtain an overview of the most important examples of
  linear and generalized linear models, and to derive theoretical
  properties of new models based on the general theory;
• recognize
  the importance of and the difference between regression and dispersion
  parameters, and use this knowledge in practical and theoretical
  contexts:
• carry out practical data analysis using statistical
  modeling, including investigation of a model’s adequacy using residual
  analysis;
• perform the statistical analysis using the statistical
  software R, including the ability to identify and interpret relevant
  information in the program output;
• document the results of a statistical analysis in the form of a written report.

In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours or the training phase hours.

Educational activities 

The students are expected to:

• Work with the new concepts and terms introduced.
• Increase their understanding of the topics covered during the lectures.
• Solve relevant exercises.
• Read the text book chapters and the scientific journal articles provided as support for the lectures