PAP334 Statistical Methods

1. Course title

Tilastolliset menetelmät
Statistiska metoder
Statistical Methods

2. Course code

PAP334

Aikaisemmat leikkaavat opintojaksot 530088 Tilastolliset menetelmät, 5 op.

3. Course status: optional

-Which degree programme is responsible for the course?
Master’s Programme in Particle Physics and Astrophysical Sciences

-Which module does the course belong to?
PAP300 Advanced Studies in Particle Physics and Astrophysical Sciences (optional for Study Track in Particle Physics and Cosmology)

-Is the course available to students from other degree programmes?
Yes

4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)

Master’s level, degree programmes in medicine, dentistry and veterinary medicine = secondcycle
degree/EQF level 7
Doctoral level = third-cycle (doctoral) degree/EQF level 8

-Does the course belong to basic, intermediate or advanced studies (cf. Government Decree
on University Degrees)?
Advanced studies

5. Recommended time/stage of studies for completion

-The recommended time for completion may be, e.g., after certain relevant courses have
been completed.

Can be taken at any stage of master's or doctoral studies

6. Term/teaching period when the course will be offered

The course offered every year in the autumn term, in I and II period.

7. Scope of the course in credits

5 cr

8. Teacher coordinating the course

Kenneth Österberg

9. Course learning outcomes

-Description of the learning outcomes provided to students by the course
- See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).

After the course, the student will...

1. learn to know the basics of statistics and statistical distribution as well as being able to apply the correct distribution.
2. understand hypotheses testing and different methods for hypotheses testing as well as the strengths and weaknesses of the methods.
3. understand parameter estimation based on maximum likelihood and least squares methods as well as the strengths and weaknesses of the methods.
4. being able to apply methods of hypothesis testing and parameter estimation as well as make the correct statistical interpretation.
5. being familiar with confidence intervals and unfolding.

10. Course completion methods

-Will the course be offered in the form of contact teaching, or can it be taken as a distance
learning course?
-Description of attendance requirements (e.g., X% attendance during the entire course or
during parts of it)
-Methods of completion

Course completion based on sufficient points in two out of three methods: weekly exercises based on lectures, final home exam and final written exam (optional).

11. Prerequisites

-Description of the courses or modules that must be completed before taking this course or
what other prior learning is required

Able to use some statistical library or tool (Matlab, Octave, ROOT, etc...) for numerical calculation or simulation.

Programming and usage of statistical libraries or tools are not taught during this course.

12. Recommended optional studies

-What other courses are recommended to be taken in addition to this course?
-Which other courses support the further development of the competence provided by this
course?

PAP331 Computing Methods in High Energy Physics, MATR322 Scientific Computing III and MATR323 Basics of Monte Carlo Simulations

13. Course content

-Description of the course content

• Fundamental concepts: experimental errors and their correct interpretation, frequentist & Bayesian interpretation of probability, the most common statistical distributions and their applications.
• Monte Carlo methods: basics of Monte Carlo methods and generation of an arbitrary distribution.
• Hypothesis testing: the concept of hypothesis testing, a test statistic, discriminant multivariate analysis, goodness-of-fit tests and ANOVA.
• Parameter & error estimation: the concept of parameter estimation, an estimator, the maximum likelihood method and the method of least squares.
• Confidence intervals & Unfolding: basics about setting confidence intervals and making unfolding.

14. Recommended and required literature

-What kind of literature and other materials are read during the course (reading list)?
-Which works are set reading and which are recommended as supplementary reading?

Main material:

Lecture notes;

G. Cowan: Statistical Data Analysis (Oxford University Press 1998.

Supplymentary reading: 

Particle Data Group Reviews on Probability, Statistics & Monte Carlo techniques (available at pdg.lbl.gov).

15. Activities and teaching methods in support of learning

-See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).
-Student activities
-Description of how the teacher’s activities are documented

Weekly lectures and exercises (individual work). Final exams. Total hours 135.

16. Assessment practices and criteria, grading scale

-See the competence map (https://flamma.helsinki.fi/content/res/pri/HY350274).
-The assessment practices used are directly linked to the learning outcomes and teaching
methods of the course.

Final grade based on best two out of three with equal 50 % weight: exercises, final home exam and final written exam (optional).

17. Teaching language

English