Statistiek AN, 2016-2017


The course aims at familiarising students with statistical techniques with applications to astronomical research.


The course will present fundamental notions in statistics and specific statistical techniques with applications to astronomical research. The topics covered are the basics of probability theory, point estimation, confidence intervals, hypothesis testing, regression, and others. Methods will be illustrated using the statistical software package R.


All of Statistics by Larry Wasserman (used copies are sometimes available on Amazon and elsewhere on the web; electronic version is available through the university library, and Springer sells the MyCopy Softcover Edition at a cheaper price than hard copies) and handouts. A partial use of Julian J. Faraway, Practical Regression and Anova in R, will also be made. The book can be downloaded here. Another book we will use is Christian Ritz and Jens Carl Streibig, Nonlinear Regression with R (electronic version is available through the university library, and Springer sells the MyCopy Softcover Edition at a cheaper price than hard copies). Finally, S. Andreon and B. Weaver, Bayesian Methods for the Physical Sciences, will be used for some examples (electronic version is available through the university library, and Springer sells the MyCopy Softcover Edition at a cheaper price than hard copies).


Get R installed on your computer. A short guide to R in Dutch is available here. A number of manuals in English can be found here. Since the R interface is rather basic, you might also consider installing RStudio, a free and open source integrated development environment for R (runs under Windows, Mac, or Linux). Handy tools included in R, that enable embedding the R code within LaTeX documents to generate a pdf file with analyses, graphics and the results of computations, are Sweave and knitr. They are fully supported by RStudio: a few basic directions for their use from within RStudio can be consulted here.

As the course progresses, additional R related materials will be made available.

Since Python is the programming language astronomy students in Leiden are taught, it is worth mentioning that there are two Python to R interfaces available: RPy and RSPython.


Lectures by Shota Gugushvili, exercise classes by Dong Yan.

Office hour

No fixed office hour, but you are welcome drop by (Snellius building 226). Send an email beforehand.


  • Lectures: Mondays 11.15-13.00.
  • Exercise classes: Tuesdays 09.00-10.45.


  • Lectures are in room HL 207.
  • Exercise classes are in room HL 207.

Homework assignments

The homework assignments will be made available for download in due time.


There will be a written exam (open book).

    Time: May 29, 2016, 13:45-15:30.

The exam is on topics covered in the class. For a passing grade one needs to know basic notions from probability and statistics and how to apply them to solve various exercises. Memorising specific formulae is not required, provided one is able to locate them in the textbook.

Old exams can be found here, but note that at that time a different textbook was used. For an old exam from 2015 see here.


The final grade is a weighted sum of the grade for homework assignments (30%) and the grade for the written exam (70%). The written exam can be retaken. Grades for the homework assignments remain valid for the retake.

Useful links

  • Link to the course homepage from 2012-2013.
  • Link to the Penn State 2013 Summer School in Statistics for Astronomers.

(Check back regularly. Last updated: 15 May 2017)

Wasserman, Sections 1.1-1.3. Slides, Chapter 1.
Wasserman, Sections 1.4-1.7, 2.1-2.4. Slides, Chapter 1 and Chapter 2. Exercises, Section 1.10: 2, 9, 11, 14, 17, 19; Section 2.14: 2, 4.
Wasserman, Sections 2.5-2.8, 2.11-.2.12, 3.1-3.4. Slides, Chapter 2 and Chapter 3. Exercises, Section 2.14: 13 (a), 15, 17.
Wasserman, Sections 4.1-4.2, 5.1-5.4. Slides, Chapter 4 and Chapter 5. Exercises, Section 3.8: 9, 11, 15; Section 4.5: 1, 2, 7; Section 5.8: 6, 8.
Wasserman, Sections 6.1-6.3, Chapter 7 (partially). Slides, Chapter 5 and Chapter 6. Exercises, Section 5.8: 1(a); Section 6.6: 1, 3; Section 7.4: 1, 4.
Wasserman, Section 6.3, Chapter 7 (partially). Slides, Chapter 6 and Chapter 7.
Wasserman, Chapters 7-8. Slides, Chapter 7 and Chapter 8. Exercises, Section 7.4: 2, 4, 7, 8.
Wasserman, Sections 8.5.1, 9.1-9.7. Slides, Chapter 8 and Chapter 9. Exercises, Section 9.14: 1, 2 (a), 3 (a), 5.
Plan: Wasserman, Sections 3.4, 9.8-9.11, 14.3, 5.4-5.5, Chapter 10 (introduction). Slides, Chapter 9, part 1 and part 2, and Chapter 10. Exercises: Section 9.14: 7.
Wasserman, Sections 10.1-10.2. Slides, Chapter 10. Exercises: Section 10.11: 6, 7(a), 8, 12(a).
Wasserman, Sections 10.3-10.8, 10.10.2, 14.2, 14.4. Slides, Chapter 10, and additional slides, part 1 and part 2. Exercises: Section 10.11: 6, 7(a), 8, 12(a).
Plan: Wasserman, Sections 13.1-13.5; Selected material from Faraway, Chapters 2, 3, 7, 8.1, 10. Slides, Chapter 13, and regression diagnostics. Exercises: Section 13.10: 3, 5, 6.
Wasserman, Sections 13.6, 20.1, 20.4 (all very briefly); Faraway, Chapter 10 (briefly); nonlinear regression example from astronomy (galaxy surface brightness profile for NGC 4472 (Messier 49)). Slides on model selection and nonparametric regression (by Chad M. Schafer.

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