# NMAK14029U Statistics for Bioinformatics and eScience (StatBI/E)

Volume 2023/2024
Education

MSc Programme in Bioinformatics

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.
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.
IMPORTANT: This course requires and assumes quantitative/​mathematical prior knowledge equivalent to a MatIntro or equivalent course!

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

Academic qualifications equivalent to a BSc degree is recommended.
5 hours of lectures and 3 hours of exercises per week. 7 weeks of classes.
• Category
• Hours
• Lectures
• 42
• Preparation
• 110
• Practical exercises
• 24
• Exam
• 30
• Total
• 206
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
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 two individual online assignments in form of quizzes of 1.5 hours each, which will be taken as part of the teaching.

For the final grade part (1) weighs 60% and part (2) weighs 40%.
All parts need to be completed individually.
Aid
All aids allowed
Marking scale
Censorship form
No external censorship
One internal examiner
Re-exam

The re-exam of part (1) takes the form of a 20 minutes oral exam without preparation. The re-exam of part (2) takes the same form as the ordinary part-exam.

Successfully passed part-exams do not have to be repeated; yet, students can choose to participate in the various part-re-exams in which case they need to inform the course responsible (at least 4 weeks before the re-exam) if they wish to repeat it. Results of part-exams that are not repeated will be included in the assessment of the re-exam with the result obtained when they were taken the first time.

If ten or fewer students have signed up for the re-exam, the type of assessment will be changed to a 30 minutes oral exam with 30 minutes preparation. All aids allowed.

##### Criteria for exam assesment

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