Course code 07 59 1112 60
ECTS credits 4
Course title in the language of instruction
Fundamentals of Data Analysis
Course title in Polish Fundamentals of Experimental Data Analysis (Podstawy analizy danych doświadczalnych)
Course title in English
Fundamentals of Data Analysis
Language of instruction English
Course level first-cycle programme
Course coordinator dr inż. Ewa Pastorczak
Course instructors
Delivery methods and course duration
Lecture Tutorials Laboratory Project Seminar Other Total of teaching hours during semester
Contact hours 20 20 20 0 60
E-learning No No No No No No
Assessment criteria (weightage) 0.30 0.40 0.30 0.00
Course objective
  1. To familiarize students with the theory of errors.
  2. To familiarize students with statistical methods of analysis of experimental data.
  3. To familiarize students with the preparation of measurement systems and procedures.
  4. To prepare students for the proper collection, analysis and presentation of experimental data.
Learning outcomes
  1. Student should know how to optimally design and assemble the measuring system (FFT1A_W19, FFT1A_U18)
  2. Student should be able to identify sources of measurement errors (FFT1A_W19, FFT1A_W11)
  3. Student should be able to interpret the results of statistical tests (FFT1A_W04, FFT1A_U15)
  4. Student should be able to carry out statistical analysis of measurement results and errors (FFT1A_U15)
  5. Student should know how to properly collect and present the results of the measurements (FFT1A_U21)
Assessment methods
The written test (LO 1-2)
Practical test (LO 3-4)
Tutorial sessions and laboratory experiments (LO 2 -5)
Raports from experiments (LO 4-5)

Final assessment is the weighted average from theoretical test (30%), practical test (40%) and the mean
assessment of the laboratory activities (30%)
Prerequisites
Secondary school knowledge of mathematics
Course content with delivery methods
LECTURE
1. Deduction and induction. Organization of the experimental research. Research planning.
2. Error theory. Kinds of error. Uncertainty class. Applications of the Error theory.
3. Selected topics from the probability and statistics. Statistical distributions applied in physics and data analysis. Test for nonrandomness.
4. Statistical hypotheses. Parameters of the distribution. Parametric estimation - most important estimators. Selected statistical programmes.
5. Statistical hypothesis testing. Parametrical and non-parametrical tests.
6. Managing data sets. Outliers. The rule of the huge error. The Dixon, Grubbs, Youden and Cochran Tests
7. Correlation and regression. Two-variable case. Correlation and regression for more than two variables.
8. Methods of the parametrical estimation. The Least Square Method and Maximum Likelihood Method.
9. Improving measurement precision. Application of the Fourier Transform. Smoothing. Calculations. Verification of the algebraic. Extrapolation and interpolation. Commercial mathematical programms.
10. Presenting data. Charts and graphs. Selected graphical and presentation applications. Scientific publication preparing.

TUTORIALS
1. Verification of the parametric hypothesis
2. Verification of the non-parametric hypothesis
3. Correlation and regression
4. Least square method
5. Extrapolation and interpolation

LABORATORY

Students conduct simple experiments and prepare reports
Basic reference materials
  1. R. Lyman Ott, An Introduction to Statistical Methods and Data Analysis, Duxbury Press 1984
  2. O. Andersson, Experiment!: Planning, Implementing and Interpreting, John Wiley & Sons, 2012
Other reference materials
  1. Linnik J.W., "Metoda najmniejszych kwadratów i teoria opracowywania obserwacji", PWN, Warszawa 1962.
  2. Gajek L., Kaluszka M., "Wnioskowanie statystyczne. Modele i metody", WNT, Warszawa 1994.
  3. Squires G.L., "Practical Physics", Cambridge University Press 2001.
  4. Abramowicz H., "Jak analizowac wyniki pomiarów", PWN, Warszawa 1992.
  5. J. R. Taylor, An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books, 1997.
Average student workload outside classroom
27
Comments
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