Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTICAL DATA ANALYSIS 200100715121 3 + 0 3.0 7.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals Statistical distributions used in data analysis, error, statistical precision value, signal and background calculations, Monte Carlo event productions are aimed to be applied to basic particle collisions and decays.
Course Content Basic concepts, probability functions, statistical tests, prediction of parameters, maximum probability methods, minimum square methods, reliability level, multi variable fit, multi dimensional reliability zone, Monte Carlo calculations, phase space distribution, data analyze.
Learning Outcomes 1) Gains talent for usage of mathematical and physical methodes to study accelerator and related subjects.
2) Interpares the results using various experimantal and measurement techniques in different discipline
3) Evaluates kinds and specifications of particles, particle sources, high energy particle interactions, radiation kinds and sources and their theories using scientific aprroach.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction Lecture
Brainstorming
Brain Based Learning 		Presentation (Including Preparation Time)
2. Week Basic concepts Lecture; Question Answer
Brainstorming; Opinion Pool
Storyline 		Presentation (Including Preparation Time)
3. Week Probability functions Lecture; Question Answer; Problem Solving
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
4. Week Statistical tests Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time) Project (Including Preparation and presentation Time)
5. Week Prediction of parameters, Question Answer; Problem Solving
Opinion Pool; Colloquium
Project Based Learning; Brain Based Learning 		Report (Including Preparation and presentation Time)
6. Week Maximum probability methods, minimum square methods Question Answer; Problem Solving
Opinion Pool
Project Based Learning; Brain Based Learning 		Project (Including Preparation and presentation Time)
7. Week Midterm Problem Solving
Debate
Brain Based Learning 		Presentation (Including Preparation Time)
8. Week Statistical Errors Question Answer; Problem Solving
Brainstorming; Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
9. Week Statistical Errors Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Homework
10. Week Reliability level Lecture; Question Answer
Brainstorming
Project Based Learning; Brain Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
11. Week Multi variable fit, multi dimensional reliability zone Problem Solving
Brainstorming; Colloquium
Project Based Learning; Brain Based Learning 		Project (Including Preparation and presentation Time)
12. Week Monte Carlo calculations Lecture; Question Answer
Brainstorming; Speech Loop
Storyline; Brain Based Learning 		Presentation (Including Preparation Time)
13. Week Monte Carlo calculations Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Homework
14. Week Phase space distribution, data analyze. Lecture; Question Answer
Opinion Pool
Project Based Learning 		Project (Including Preparation and presentation Time)

Sources Used in This Course
Recommended Sources
A Guide to the Use of Statistical Methods in the Physical Sciences (Manchester Physics Series) by Prof. R. Barlow. ISBN-10: 0471922951
G. Cowan, Lectures on Statistical Data Analysis, www.pp.rhul.ac.uk/~cowan/stat_course.html, ISBN-10: 0198501552
Statistical Methods in Experimental Physics (World Scientific) by F. James. ISBN-10: 9812705279

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15000
PY25000
PY35000
PY45000
PY55000
PY84444
PY95555
PY104444
PY113333
PY124444
PY135555
PY144444
PY154444
PY174444
PY184444

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 5
Homework 3 5
Presentation (Including Preparation Time) 1 20
Report (Including Preparation and presentation Time) 1 30
Final Exam 1 5
Time to prepare for Final Exam 1 30
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload