Autumn 2021 lectures and exercises are held in the Zoom-link specified below.

The period analysis method **
Discrete Chi-square Method (DCM) ** is formulated in

- | Jetsu (2020: The Open Journal of Astrophysics, vol. 3, issue 1, id. 4) | "Discrete Chi-square Method for Detecting Many Signals" (Paper I).

- |dcm.py |
- |dcm.dat|
- |fisher.py|
- |TestData.dat|

to the same directory in your computer. We apply DCM to simulated data. DCM is also compared to other time series analysis methods, like the Discrete Fourier Transform (DFT).

** Current version
** of

- |Line.py|
- |LineModel.py|
- |TrendSine.dat|
- |Scargle.dat|
- |TrendDFTData.dat|
- |ExerciseScargle.py|
- |ExampleBoostrap.py|
- |SimulationXXX.dat|
- |ExercisePreWhitenData.dat|
- 0/2 (Exercise not done at all, or it makes no sense)
- 1/2 (Exercise is a good try)
- 2/2 (Exercise is correct, or nearly correct)
- 0/5 = <45% of NMAX
- 1/5 = 45-55% of NMAX
- 2/5 = 55-65% of NMAX
- 3/5 = 65-75% of NMAX
- 4/5 = 75-85% of NMAX
- 5/5 = <85% of NMAX
- Autumn 2021
- Autumn 2019

There are no exams. The students will perform assignments. The number of assignments completed by the student determines the grade received of this course. There will be N=10-12 exercises. The evaluation criteria of each Exercise are

The maximum points are NMAX = 2 x N pts. The grade limits are

** Points/Grades **