ST520: Applied Statistics

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N360002102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor

STADS ID (UVA): N360002101
ECTS value: 5

Date of Approval: 05-10-2022


Duration: 1 semester

Version: Approved - active

Entry requirements

The course cannot be followed by students who: have followed ST503 statistics for biologists.

Academic preconditions

  • It is expected that the students have a mathematical knowledge corresponding to the content in one of the following courses: FF506 Mathematics, statistics and physics for biology and pharmacy; MM554: Mathematics for biology; MM555: Mathematics for Biochemistry and Molecular Biology, Biomedicine and Chemistry; MM556: Mathematics and statistics for pharmacy.
  • First year of respective study programmes.

Course introduction

The course has as purpose to enable the students to:
  • Understand concepts in probability and distribution theory.
  • Utilize graphics and summary methods for descriptive data analysis.
  • Describe data using key statistics such as mean, variance, and correlation.
  • Construct confidence intervals for key statistics.
  • Test simple statistical hypotheses.
  • Analyze data using simple regression models.
  • Design data collection.
  • Understand central elements in published results from statistical analyze of biological data.
  • Critically evaluate the appropriateness of employed methods and inferences based on these.
  • Present statistical results in non-technical terms.
  • Use the statistical software R for analysing actual data, which is important in regard to being able to work academically-scientifically with – in a broad sense – biological problems.
The course builds on the knowledge acquired in the courses in the first or first two years of the respective study programmes; and gives an academic basis for studying the all later topics in the curriculum, as well as the bachelor and master projects.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • Give the competence to working critically with own projects and data.
  • Give skills to critival evaluate scientific publications.
  • Give knowledge and understanding of choice and use of appropriate statistical methods.

Expected learning outcome

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

  • Utilizing graphics and summary methods for descriptive data analysis.
  • Describing data using key statistics such as mean, variance, and correlation.
  • Constructing confidence intervals for key statistics.
  • Testing simple statistical hypotheses.
  • Analyzing data using simple regression models.
  • Designing data collection.
  • Understanding central elements in published results from statistical analyses of biological data.
  • Critically evaluating the appropriateness of employed methods and inferences based on these.
  • Presenting statistical results in non-technical terms.
  • Use R for simple statistical analyses.

Content

The following main topics are contained in the course:
  • The foundation for statistical considerations. 
  • From population to sample and back again. 
  • Basic Parameters and their estimation. 
  • Descriptive statistics (tables and graphics). 
  • Probabilities and distributions. 
  • Hypotheses and principles for tests. 
  • Examples of test methods: t-test, chi-square-test. 
  • Basic concepts underlying linear models starting from simple linear regression. 
  • Basic concepts with regard to study design. 
  • Common problems in applied statistics (types of inferential error, mass significance, pseudoreplication).
  • In the course the statistical software R is used.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Spring

Tests

Portfolio

EKA

N360002102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Additional information

Portfolio consisting of mandatory e-test, quizzes and written home assignments

Form of re-examination:
In case of 11 or more students signed up for re-exam, the re-exam will be in the form of an home assignment consisting of an etest and a written assignnment. The etest and the written assignment are weighted equally.
In case of 10 or fewer students signed up for re-exam, the re-exam will be in the form of an oral exam based on a number of home assignments prepared before the oral exam.

Indicative number of lessons

48 hours per semester

Teaching Method

These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:

  • Intro phase (lectures) - 26 hours
  • Training phase: 22 hours

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 aor the study phase hours.

Educational activities 

  • Work on specific problems not covered in the training phase hours.
  • Discussion of the terms and concepts and problems in regard to data collection and data quality.

Teacher responsible

Name E-mail Department
Hans Chr. Petersen hcpetersen@sdu.dk Data Science

Timetable

Administrative Unit

Institut for Matematik og Datalogi (matematik)

Team at Educational Law & Registration

NAT

Offered in

Odense

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Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.