TAMS65 · 6 credits · Basic level (G2F) · Matematik, Tillämpad matematik
Mathematical Statistics, Second Course
Active · next start VT 2027
73%
passed the latest ordinary exam
TEN2 · 25 Mar 2026 · 193 students
85%
passed the ordinary exams, all sittings over the last 5 years
TEN2 · 900 students
Unusually hard in 2025/26
73% passed the ordinary exam in 2025/26 (193 students), against 88% on average in earlier years (707 students).
Part of 2 programmes
Exam
TEN2 Written exam
- 5
- 4
- 3
- U
Ordinary exams
85% passed · 900 students
- 73%193
- 88%181
- 91%176
- 92%170
- 82%180
Re-exams
57% passed · 274 students
- 60%25
- 57%44
- 17%12
- 68%34
- 14%14
- 52%23
- 77%22
- 71%21
- 69%36
- 44%43
- Pass the re-exam: 57% (274 students), compared with 85% (900 students) on the ordinary exam
- Get 4 or 5: 54% (1,174 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG2 Project assignment
- G
Main rounds
100% passed · 945 students
- 100%199
- 100%189
- 100%183
- 100%181
- 100%193
Later rounds
No sittings with at least 5 students.
Show small sittings (9)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%1
- 100%3
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
About the course
Aim
The course is intended to give basic knowledge of the theory and methods of statistical inference, i.e. how to use observed data to draw conclusions about phenomena influenced by random factors. By the end of the course, the student should be able to:
- M1 (Point estimation): use an appropriate random model to describe and analyze observed data and draw conclusions about parameters of interest, and derive point estimates of parameters and analyze their properties.
- M2 (Interval Estimation and Hypothesis Testing): understand the principles of drawing conclusions via confidence intervals and hypothesis testing. Construct confidence intervals and conduct hypothesis testing for observed data, report conclusions and assess certainty.
- M3 (Chi-square test): determine whether a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.
- M4 (Multiple regression analysis): analyze relationships between variables using simple or multiple linear regression and assess the relevance of the model used. Apply random models and statistical methods in connection with questions in economics, technology and natural sciences and critically review the results.
- M5 (Software): use an appropriate software package (e.g. Matlab, R or similar) for certain types of statistical analyses.
Content
Chi-square-, t-, F-distribution. Point estimation, properties of estimators, the method of maximum likelihood, the method of moments and the least squares method. Confidence intervals and tests of hypotheses for one or several samples especially for normal, binomial and Poisson distribution and when the central limit theorem can be applied. Chisquare tests. Random vectors, mean vectors and covariance matrices. The multivariate normal distribution.Multiple regression, estimation of parameters, confidence intervals, prediction, analysis of variance table, selection of variables and transformations.Suitable statistical software is used for regression analysis.
- Examiner
- Zhenxia Liu
- Scheduled hours
- 56 h
- Main field of study
- Matematik, Tillämpad matematik