# 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.
• 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.

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.

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.

ECTS
7,5 ECTS
Type of assessment
Written assignment, 30 hours
2 days take-home assignment.
Marking scale
Censorship form
No external censorship
One internal examiner
##### Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.

Single subject courses (day)

• Category
• Hours
• Lectures
• 35
• Practical exercises
• 21
• Preparation
• 90
• Project work
• 30
• Exam
• 30
• English
• 206