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

Volume 2021/2022
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.
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
• 35
• Preparation
• 120
• Practical exercises
• 21
• Exam
• 30
• Total
• 206
Continuous feedback during the course of the semester
Credit
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
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 may 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.