Statistics for Bioinformatics and eScience (StatBI/E)
Course content
The course is based on a set of concrete cases that will take the participants through the following content.
- Standard discrete and continuous distributions, descriptive methods, the frequency and Bayesian interpretations, conditioning, independence, and selected probability results.
- Simulation.
- Mean, variance, estimators, two-sample comparisons, multiple testing.
- Maximum likelihood and least squares estimation.
- Standard errors and confidence intervals.
- Bootstrapping.
- Correlation, linear, non-linear, logistic and Poisson regression.
- The statistical programming language R.
- Models for neuron activity, gene expression, database searches, motif and word occurrences, internet traffic, diagnostic tests etc.
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
- Linear, non-linear, logistic and Poisson regression
Skills:
- Master practical implementation in R.
- 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 linear, non-linear, logistic and Poisson regression models.
Teaching and learning methods
5 hours of lectures and 3 hours of exercises per week. 7 weeks of classes.
Recommended prerequisites
MSc students and BSc students in their 3rd year with MatIntro or
an equivalent course.
Academic qualifications equivalent to a BSc degree is
recommended.
Feedback form
Continuous feedback during the course of the
semester
Exam
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 30 hoursTake-home assignment.
- 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.
Course type
Single subject courses (day)
Workload
- Category
- Hours
- Lectures
- 35
- Preparation
- 90
- Practical exercises
- 21
- Project work
- 30
- Exam
- 30
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK14029U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Schedulegroup
-
C
- Capacity
- No limit.
- Studyboard
- Study Board for the Biological Area
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Sebastian Weichwald (10-7a7e6c706a6f7e68736b4774687b6f35727c356b72)
Saved on the
07-05-2020
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