• Svenska

# SF1911 Statistics for Bioengineering 6.0 credits

### For course offering

Autumn 2024 Start 28 Oct 2024 programme students

### Target group

No information inserted

P2 (6.0 hp)

28 Oct 2024
13 Jan 2025

33%

Normal Daytime

Swedish

KTH Campus

### Number of places

Places are not limited

## Application

### For course offering

Autumn 2024 Start 28 Oct 2024 programme students

50753

## Contact

### For course offering

Autumn 2024 Start 28 Oct 2024 programme students

### Contact

Camilla Johansson Landén (landen@kth.se)

### Examiner

No information inserted

### Course coordinator

No information inserted

### Teachers

No information inserted
Headings with content from the Course syllabus SF1911 (Autumn 2020–) are denoted with an asterisk ( )

## Content and learning outcomes

### Course contents

The course treats the most important practical statistical methods used in bioengineering and biomedical engineering and during the course these methods are implemented in software familiar to engineering students. The course focuses primarily on the practical aspects of statistics in biotechnology. Computer-aided exercise work with a variety of datasets constitutes an essential learning activity.

More specifically, the course contains the following topics:

• Bioengineering data and descriptive statistics, both visual and numeric presentation.
• Basic concepts such as probability, conditional probability and independent events. Bayes’ formula. Discrete and continuous random variables, in particular one dimensional random variables. Measures of central tendency, dispersion and dependence of random variables and data sets. Common distributions and models, such as the normal, exponential, Poisson and uniform distributions. The Central limit theorem.
• Point estimates and general methods of estimation, such as maximum likelihood estimation and the method of least squares. Evaluation and comparison of point estimates, for example with respect to bias and efficiency. Confidence intervals and p-values. Two sample problems. Statistical hypothesis testing. Chi2-tests of goodness of fit, homogeneity and independence. Odds ratios. One- and two-way ANOVA. Design of experiments.

### Intended learning outcomes

To pass the course, the student should be able to

• solve problems that require knowledge about standard concepts and methods in probability theory
• solve problems that require knowledge about standard concepts and methods in statistics
• judge the applicability and limitations of different statistical methods and interpret statistical analyses of bioengineering data

## Literature and preparations

### Specific prerequisites

• Completed course SF1524 Basic Numerical Methods and Programming.
• Completed course in Calculus in One Variable SF1625.

### Recommended prerequisites

SF1624 Algebra and Geometry, SF1626 Calculus in Several Variable

### Equipment

No information inserted

### Literature

No information inserted

## Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

A, B, C, D, E, FX, F

### Examination

• LABA - Laboratory work, 1.5 credits, grading scale: P, F
• TENA - Written exam, 4.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

### Opportunity to complete the requirements via supplementary examination

No information inserted

### Opportunity to raise an approved grade via renewed examination

No information inserted

### Ethical approach

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

## Further information

### Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Technology

First cycle