NSCPHD1100 Applied Statistical Modelling in Forest and Natural Resource Assessment and Planning

Volume 2015/2016
Education

PhD

Content

 

 

PLEASE NOTE         

The PhD course database is under construction. If you want to sign up for this course, please click on the link in order to be re-directed. Link: https://phdcourses.ku.dk/nat.aspx

 

Prior to the course, students will read the course portifolio, mainly consisting of scientific articles related to the course subject. Further, the students will choose a relevant dataset to analyze during the seminar in week 5. If a student lack a relevant dataset, the course responsible will provide one.

After the course, the students will prepare a written report on the analyses conducted and their results.

A satisfactury evaluation of the report is necessary to pass the course.

Learning Outcome

Knowledge

  • describe principles for selecting appropriate statistical models and calibration procedures for different types of data
  • understand the relationship between statistical analysis and development of models
  • show overview of model types used to describe relationships and to model natural resource dynamics

Skills

  • apply statistical principles and methods in typical natural resource modelling situations
  • select suitable model formulations for modelling particular relationships
  • apply suitable methods for assessing the quality of predictions

Competences

  • apply general modelling and forecasting principles, involving typical variables from forest and nature
  • discuss the relevance, reliability and interpretation of empirical statistics

To be decided

The students are required to have basic statistical competences in statistics and regression analysis.
Students are expected to work independently with their own material prior to and after the course seminar. During the seminar week, students will recieve lectures detailing common approaches in statistical modelling and working with small examples. After lectures, students will work with their own datasets, recieving supervision by the course team.
  • Category
  • Hours
  • Class Instruction
  • 40
  • Preparation
  • 40
  • Project work
  • 40
  • Total
  • 120
Credit
4 ECTS
Type of assessment
Written assignment, 1 uge under invigilation
After the course, the students will prepare a written report on the analyses conducted and their results.

A satisfactury evaluation of the report is necessary to pass the course.
Exam registration requirements

To have participated in the seminar

Aid
All aids allowed
Marking scale
passed/not passed