NMAK14029U Statistics for Bioinformatics and eScience (StatBI/E)
MSc Programme in Bioinformatics
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.
- Mean, variance, estimators, two-sample comparisons, multiple testing.
- Maximum likelihood and least squares estimation.
- Standard errors and confidence intervals.
- Correlation, linear, non-linear, logistic and Poisson regression.
- Dimensionality reduction, model selection and model validation.
- The statistical programming language R.
- Models for neuron activity, gene expression, database searches, motif and word occurrences, internet traffic, diagnostic tests etc.
The basic concepts in mathematical statistics, such as;
- Probability distributions
- Standard errors and confidence intervals
- Maximum likelihood and least squares estimation
- Hypothesis testing and p-values
- Linear, non-linear, logistic and Poisson regression
- 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.
- 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.
- 7,5 ECTS
- Type of assessment
- Written assignment, 30 hoursTake-home assignment.
- Exam registration requirements
Approval of a midd way group project report.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
Same as ordinary. If ten or fewer students have signed up for re-exam, the type of assessment will be changed to a 30 minutes oral exam with 30 minutes preparation. All aids allowed.
If the midd way group project report is not approved before the ordinary exam it must be re-submitted no later than two weeks before the beginning of the re-exam week. It must be approved before the re-exam.
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.
- Practical exercises
- Project work