NSCPHD1109 Applied Statistics with R for The Agricultural, Life and Veterinary Sciences (generic course)

Volume 2013/2014
Content
Development and advancement of agriculture, veterinary and other life sciences are linked to well-designed experiments, be it in test tubes or in the field, and to the proper use of statistical analysis. Analysis of experiments is crucial for decision making and prediction and choice of appropriate statistical tools is of paramount importance. One programme has caught the interest of theoretical statisticians as well as practical scientists and that is the open source programme and environment

See also the course's NOVA homepage: http://www.nova-university.org/page.cfm?open=7&MenySidor_id=28
Learning Outcome
To introduce the participants to a wide range of versatile methods for statistical analysis of experiments in the agricultural, life and veterinary sciences. The course focuses on how to carry out analysis of variance and regression and related models in a broad sense, using both parametric and semi-parametric approaches, by means of the statistical software programme and environment R.
The need to formulate and test hypotheses based on the design and purpose of the experiments is emphasised The series of courses will focus specifically on:

ANOVA models
Linear and non-linear regression
Categorical data
Mixed-effects models
Multivariate methods

In addition participants are taught how to organise statistical analysis using R and auxiliary open-source programmes.

Within each course all topics above will be covered, but the weighing may differ, as the course content to some extent can be adapted to the background of the participants.

http:/​/​www.r-project.org/​ Contributed Documentation. Lecture notes will be provided.

Course plan is at the course's NOVA homepage Each participant has to submit a report in article format, but with an extended and more substantial statistics section than usually required in International journals. The students preferably apply statistical methods with R to their own data (or if not available then to hand-out data). Reports have to be delivered after the course and subsequently to be approved by the teachers
  • Category
  • Hours
  • Lectures
  • 12
  • Practical exercises
  • 60
  • Preparation
  • 40
  • Project work
  • 60
  • Total
  • 172
Credit
6 ECTS
Type of assessment
Written assignment
Exam registration requirements
Participate in the one week teaching and get the assignment approved