TNG006 · 6 credits · Basic level (G2F) · Matematik, Tillämpad matematik
Statistics
Active · next start VT 2027
85%
passed the latest ordinary exam
TEN1 · 4 Jun 2026 · 52 students
89%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 354 students
36%
Harder than 36% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 117 students.
Part of 3 programmes
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
89% passed · 354 students
- 85%52
- 85%65
- 90%71
- 93%82
- 89%84
Re-exams
61% passed · 57 students
- 83%6
- 20%5
- 82%11
- 78%9
- 33%12
- 44%9
- 100%5
- Pass the re-exam: 61% (57 students), compared with 89% (354 students) on the ordinary exam
- Get 4 or 5: 57% (411 students)
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 50%2
- 100%1
- 50%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Other module
KTR1 Written test
- G
- U
Main rounds
89% passed · 304 students
- 100%63
- 88%73
- 86%80
- 84%88
Later rounds
68% passed · 28 students
- 68%28
- Pass a later round: 68% (28 students), compared with 89% (304 students) on the ordinary exam
About the course
Aim
The course is intended to teach students to understand and use basic probability and statistical theory, that is, the theory for dealing with random experiments. The emphasis is on developing the statistical background required for use in engineering, economy and natural sciences. After the course the students should be able to:
- Model and use both events and stochastic variables in different problems
- Use conditional probability
- Use the multiplication rule and combinations
- Good understanding, derivation and use of distribution functions
- Decide whether given random variables are independent
- Calculate the expected value and variance for functions of stochastic variables
- Use the Central Limit Theorem
- Point estimation by Maximum likelihood, moment and least squares methods
- Determine interval of confidence for mean and variance
- Testing a hypothesis
- Use regression analysis
Content
- Probability theory: Sample space, events and probabilities. Combinatorics. Conditional probabilities and independent events. Discrete and continuous random variables, their probability distributions, expectations and variances. Normal, exponential, binomial, Poisson distributions etc. Functions of random variables. The central limit theorem.
- Statistics: Point estimation. Properties of estimators. The method of maximum likelihood, the method of moments and the least squares estimation. Confidence intervals. Testing statistical hypotheses. Simple linear regression.
- Examiner
- Tomas Johansson
- Scheduled hours
- 52 h
- Main field of study
- Matematik, Tillämpad matematik