Statistics for Bioinformatics and eScience (StatBI/E)

Course content

The course will take the participants through the following content.

 

  • Standard discrete and continuous distributions, descriptive methods,  Bayes’ theorem, conditioning, independence, and selected probability results.
  • Simulation.
  • Mean, variance, estimators, two-sample comparisons.
  • Maximum likelihood and least squares estimation.
  • Standard errors and confidence intervals.
  • Bootstrapping.
  • Correlation, (generalized) linear and non-linear regression.
  • The statistical programming language R and R notebooks.
Education

MSc Programme in Bioinformatics

Learning outcome

Knowledge:

The basic concepts in mathematical statistics, such as;

  • Probability distributions
  • Standard errors and confidence intervals
  • Maximum likelihood and least squares estimation
  • Bootstrapping
  • Hypothesis testing and p-values
  • (Generalized) Linear and non-linear regression


Skills:

  • Master basic implementation in R and generation of analysis reports using R notebooks.
  • Use computer simulations for computations with probability distributions, including bootstrapping.
  • Compute uncertainty measures, such as standard errors and confidence intervals, for estimated parameters.
  • Compute predictions based on regression models taking into account the uncertainty of the predictions.
  • Assess a fitted distribution using descriptive methods.
  • Use general purpose methods, such as the method of least squares and maximum likelihood, to fit probability distributions to empirical data.
  • Summarize empirical data and compute relevant descriptive statistics for discrete and continuous probability distributions.


Competences:

  • Formulate scientific questions in statistical terms.
  • Interpret and report the conclusions of a practical data analysis.
  • Assess the fit of a regression model based on diagnostic quantities and plots.
  • Investigate scientific questions that are formulated in terms of comparisons of distributions or parameters by statistical methods.
  • Investigate scientific questions regarding association in terms of (generalized) linear and non-linear regression models.

5 hours of lectures and 3 hours of exercises per week. 7 weeks of classes.

MSc students and BSc students in their 3rd year with MatIntro or an equivalent course.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Continuous assessment
The exam consists of two parts: (1) two quiz assignments (60%), and (2) a 30-hours written take-home assignment (40%) in course week 8.
The first part consist of online assignments in form of quizzes; students need to upload their written derivations for their solutions to the quiz questions and submit their final answers via the quiz form; students need to submit their solutions within a week after each quiz is being made available on the course webpage.
All parts need to be completed individually.
Each part-exam is assessed and weighted individually, and the final grade is determined based on this. Students can pass the exam without passing all part-exams if the total grade is 02 or higher.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner
Criteria for exam assessment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 120
  • Practical exercises
  • 21
  • Exam
  • 30
  • English
  • 206