Applied Statistics - From data to results (Fall 2011)
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"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] |
General information:
| Lecturer: | Troels C. Petersen (NBI High Energy Physics (HEP)) (petersen nbi.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. |
Course outline:
Week 1: (Introduction, setting up, general concepts)
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.
Week 2:
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.
Week 3:
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.
Week 4:
26: Sascha teaching class. Working on 1st project.
27: Likelihood and more fitting.
30: Hand out 2nd problem set (for Monday the 10th).
Week 5:
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.
Week 6:
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.
Week 7: (efterårsferie/project2)
17:
18:
21:
Week 8:
24: Calibration and use of control channels. Blind analysis and when to reject data points.
25: Evaluation of 2nd projects (with presentations)
28: Free!
Week 9:
31: Free!
1: Free!
3: Exam given (posted on course webpage in the morning).
4: Exam to be handed in.
Course weekly pages (link to lectures and exercises):
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.
Problem sets, projects, and exam set:
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:
Notes:
In addition to the text book and other litterature, some notes will be used during the course:
Links: