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SF1910 Applied Statistics 7.5 credits

The overall purpose of the course is that the student should be well acquainted with basic concepts, theory, models and solution methods in probability theory and statistical inference.

Information per course offering

Termin

Information for Autumn 2024 CSAMH2 programme students

Course location

KTH Campus

Duration
28 Oct 2024 - 13 Jan 2025
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

50042

Form of study

Normal Daytime

Language of instruction

Swedish

Course memo
Course memo is not published
Number of places

Places are not limited

Target group

Only CSAMH2

Planned modular schedule
[object Object]

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted
Contact

Björn-Olof Skytt (bos@kth.se)

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus SF1910 (Autumn 2019–)
Headings with content from the Course syllabus SF1910 (Autumn 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Basic concepts such as probability, conditional probability and independent events. 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, binomial and Poisson distributions. The Central limit theorem and the Law of large numbers.

Descriptive statistics, both visual and numerical presentation.

Point estimates and general methods of estimation, such as maximum likelihood estimation and the method of least squares. General confidence intervals and in particular confidence intervals for the mean and variance of normally distributed data. Confidence intervals for proportions and for difference in means and proportions.

Statistical hypothesis testing. Chi2-tests of goodness of fit, homogeneity and independence. Linear regression.

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
  • carry out project work in a group with larger and realistic data sets and use statistical methods to support decisions that can support sustainable development

Literature and preparations

Specific prerequisites

Completed course in SF1625 Calculus in one variable. 

Recommended prerequisites

SF1626 Calculus in Several Variable, SF1624 Algebra and Geometry 

Equipment

No information inserted

Literature

Blom et al., Sannolikhetsteori och statistikteori med tillämpningar, Studentlitteratur

Complemental material from the department.

Examination and completion

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

Grading scale

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

Examination

  • PRO1 - Project, 1.5 credits, grading scale: P, F
  • TEN1 - Examination, 6.0 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.

The examiner decides, in consultation with KTHs Coordinator of students with disabilities (Funka), about any customized examination for students with documented, lasting disability. 

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

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.

Offered by

Main field of study

Technology

Education cycle

First cycle

Add-on studies

No information inserted

Contact

Björn-Olof Skytt (bos@kth.se)