732A83 · 9 credits · Advanced level (A1N) · Statistik
Statistical Methods
Active · next start HT 2026
88%
passed the latest ordinary exam
TEN2 · 11 Dec 2025 · 26 students
86%
passed the ordinary exams, all sittings over the last 5 years
TEN2 · 51 students
49%
Harder than 49% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 51 students.
Part of 1 programme
Exam
TEN2 Examination
- A
- B
- C
- D
- E
- F
Ordinary exams
86% passed · 51 students
- 88%26
- 84%25
Re-exams
82% passed · 11 students
- 100%5
- 67%6
- Pass the re-exam: 82% (11 students), compared with 86% (51 students) on the ordinary exam
- Get B or A: 56% (62 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 50%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Exam
TEN1 Examination
- A
- B
- D
- E
- F
Ordinary exams
54% passed · 26 students
- 54%26
Re-exams
No sittings with at least 5 students.
- Get B or A: 19% (26 students)
Other module
FRIV Voluntary Assignment
- P
Main rounds
100% passed · 27 students
- 100%27
Later rounds
No sittings with at least 5 students.
About the course
Aim
After completion of the course the student shall be able to:
- apply the common statistical distributions for building statistical models
- explain and apply probability distributions for functions of random variables
- explain and apply multivariate normal distributions
- apply main principles within point estimation, interval estimation and hypothesis testing
- demonstrate and apply the main concepts of Bayesian analysis
- apply linear regression models, check their uncertainty and perform model comparison
- explain and apply common stochastic processes
- present the underlying mathematical models for the above methods and perform theoretical calculations with these models
- critically discuss the fulfillment of the models' assumptions
- carry out tasks within given time frames
Content
- concept of probability
- random variable, common statistical univariate and multivariate distributions and their properties, central limit theorem
- multivariate normal distributions
- probability distributions for functions of random variables
- stochastic processes
- point estimation – properties and methods
- interval estimation
- hypothesis testing
- simple and multiple linear regression; least squares estimation; residual and outlier analyses
- likelihood, prior and posterior distribution, Bayes’ theorem
- Examiner
- Krzysztof Bartoszek
- Main field of study
- Statistik