"Coincidences, in general, are great stumbling blocks in the way of
that class of thinkers who have been educated to know nothing of the
theory of probabilities [and statistics] - that theory to which the most glorious
objects of human research are indebted for the most glorious of
illustration."[Edgar Allan Poe, "The Murders in the Rue Morgue", 1841] |

Lecturer: |
Troels C. Petersen (NBI High Energy Physics (HEP)) (petersennbi.dk). |

Additional teacher: |
Sascha Mehlhase (NBI High Energy Physics (HEP)) (mehlhasenbi.dk). |

When: |
Monday 9-12, Tuesday 13-17, and Friday 9-12 (Week Group B). |

Where: |
Monday Aud. M, Tuesday Aud. M, and Friday Aud. M (Building M at NBI). |

Period: |
Blok 1 (5th of September - 4th of November 2011), 8 weeks. |

Evaluation: |
Exercises (25%), Problem sets and Projects (25%), Take-home exam (50%). |

Exam: |
Take-home (24 hour) exam given Thursday the 3rd of November 2011 at 8:15. |

Censur: |
Internal evaluation (following the Danish 7-step scale) |

Credits: |
7.5 ECTS (i.e. 1/8 academic years work). |

Level: |
Intended for students at 3rd - 5th year of studies and new Ph.D. students. |

Prerequisites: |
Simple mathematics and basic (but some) programming (any language). |

Programs used: |
Simple C++ and the CERN software ROOT. |

Text book: |
Roger Barlow: Statistics: A guide to the use of statistics. |

Additional litterature: |
Philip R. Bevington: Data Reduction and Error Analysis. |

Glen Cowan: Statistical Data Analysis. | |

Pensum/Curriculum: |
The course curriculum can be found here. |

Outline: |
Graduate statistics course giving an advanced introduction to data analysis. |

Course format: |
Shorter lectures followed by computer exercises and discussion. |

Key words: |
PDF, Uncertainties, Correlation, Chi-Square, Likelihood, Fitting, Monte Carlo. |

Language: |
Danish (English if requested). All exercises, problem sets, exams, notes, etc. are in English. |

5: Intro to course, photos, questionnaire, quiz, and table measurements (Aud. A). Central limit theorem. Mean, RMS and estimators.

6: Correlation. Distributions.

9: Error propagation.

12: ChiSquare and fitting. Hand out 1st problem set (for Tuesday the 20th).

13: Random numbers and their use in MC. Hypothesis testing.

16: Systematic errors. Separating/classifying events.

19: Repeating midway, including extended examples. Start 1st project (for Friday the 30th).

20: Bayes theorem. Work on 1st project.

23: No class! Working on 1st project.

26: Sascha teaching class. Working on 1st project.

27: Likelihood and more fitting.

30: Hand out 2nd problem set (for Monday the 10th).

3: Multi-Variate Analysis (MVA). Fisher and ROOT's TMVA.

4: Limits and confidence intervals. Evaluation of 1st projects.

7: Comments on fitting. Kolmogorov-Smirnov test.

10: Start 2nd project (for Tuesday the 25th).

11: Work on 2nd project (I'm gone 14:00-15:15).

14: Work on 2nd project.

17:

18:

21:

24: Calibration and use of control channels. Blind analysis and when to reject data points.

25: Evaluation of 2nd projects (with presentations)

28: Free!

31: Free!

1: Free!

3: Exam given (posted on course webpage in the morning).

4: Exam to be handed in.

The course will generally consist of (short) lectures followed by a computer problem solving session with program examples, which illustrates some of the points made in the lectures.

Here is a link to the initial questionaire.

During the course there will be problem sets to be solved, projects to be carried out, and a final take-home exam to be handed in, all of which can (in time) be found below:

In addition to the text book and other litterature, some notes will be used during the course: