SHSM13004U  Statistics for Veterinarians

Volume 2015/2016
Education

Master's Programme in Veterinary Public Healt - compulsory

Content

Aim
The student is introduced to a number of statistical techniques applied to biological examples.

Content
Topics covered are: Descriptive statistics, data types, comparison of two samples by parametric and nonparametric methods, t-tests, paired observations, basic analysis of frequency data, logistic regression, linear and multiple regression, analysis of variance, factorial experiments, analysis of covariance, repeated measurement, basic design of experiments. The statistical software system R is used extensively for analysis of examples, and participants should have access to R. Each participant should bring his/her own lap-top with a data set to be analysed; suitable data sets should be of moderate size and moderately complicated from a statistical point of view. The data set and its analysis is the subject of a project report written by each participant which is also presented at a seminar.

Learning Outcome

At the end of the course it is expected that the participant has the following qualifications:

Knowledge:
Identify a statistical problem to be solved using relevant descriptive and analytical methods.

Skills:
Collect data and evaluate the data quality and store data in a database.
Select relevant statistical methods and analyse the data.

Competences:
Collaborate scientifically wsith statisticians and other relevant scientists. Be able to evaluate the validity and reliability of the statistical results in relation to generalising to other populations than just the study population.

Lecture notes. Theory covered corresponds to:
D.G. Altman (1991). Practical Statistics for Medical Research. Chapman & Hall.
Of practical use is also:
Martinussen, Skovgaard and Sørensen (2012). A first guide to statistical computations in R. Biofolia.

 

A BSc or MSc degree in veterinary medicine, human medicine, agricultural sciences, engineering or natural science is required - and at least two years of relevant professional experience. If you wish to attend single courses, the above mentioned requirements can be deviated. Good English language skills are required.
Lectures and exercises during 6 fulldays over 5 or 6 weeks in March and April 2016. All days 8-15.
Credit
10 ECTS
Type of assessment
Practical oral examination
Oral exam based on presentation of submitted course report
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Exam period

Exam spring 2016

Criteria for exam assesment

Knowledge:
Identify a statistical problem to be solved using relevant descriptive and analytical methods.

Skills:
Collect data and evaluate the data quality and store data in a database.
Select relevant statistical methods and analyse the data.
 

  • Category
  • Hours
  • Lectures
  • 40
  • Theory exercises
  • 40
  • Project work
  • 0
  • Preparation
  • 60
  • Total
  • 140