SylabUZ
| Course name | Descriptive and Ecomonic Statistics |
| Course ID | 11.2-WK-MATP-SOE-L-S14_pNadGenDC5H1 |
| Faculty | Faculty of Mathematics, Computer Science and Econometrics |
| Field of study | Mathematics |
| Education profile | academic |
| Level of studies | First-cycle studies leading to Bachelor's degree |
| Beginning semester | winter term 2019/2020 |
| Semester | 4 |
| ECTS credits to win | 2 |
| Course type | optional |
| Teaching language | polish |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Aim of the course is to familiarize students with basics of statistical research, i.e. purposefulness of it, data mining, analysis of data, its graphical presentation and description by appropriate measures.
Mathematical analysis, probability theory and economics.
1. Data structures - an introduction to chosen statistical package (e.g. R-project). (2 teaching hrs.)
2. Classification of statistical data, their grouping and depiction in tabular format. (3)
3. Graphical presentation of the given data values. Polygons of the counts. Histograms. Pie charts. Bar charts. (3)
4. Some measures of central tendency: arithmetic mean, geometric mean, harmonic mean, median, moda. Sample quantiles. Empirical cumulative distribution function.
Quantile-quantile plot and box-and-whisker plot. (4)
5. Some measures of dispersion: range, variance, standard deviation and coefficient of variation. (2)
6. Measures of skewness. Sample kurtosis. (2)
7. Test. (2)
8. The Lorenz curve. The Gini coefficient. (2)
9. Correlation between two variables. Scatter diagram. The Pearson correlation coefficient. Linear regression. (3)
10. Dependence of the nominal variables. Contingency table. Some measures of association: the Pearson coefficient, the Cramer coefficient and the Yula coefficient. (3)
11. The Paasche index. The Laspeyres index. The Fisher index. (2)
12. Test. (2)
Application of the statistical package (e.g. R-project) and the relevant theoretical tools to analyse the data. Students present some statistical problem in the form of a project, which contains appropriate theory and tasks to the theory.
| Outcome description | Outcome symbols | Methods of verification | The class form |
1. Checking students knowledge and their active participation in laboratory.
2. Tests with the tasks of different difficulty.
3. Project evaluation.
The condition of a positive grade from laboratory is to obtain of at least 50% of the maximum sum of points from the written tests and a positive grade from the report. A grade from laboratory consists of a grade from the written tests (70%) and of a grade from report (30%).
1. I. Bąk, I. Markowicz, M. Mojsiewicz, K. Wawrzyniak, Statystyka w zadaniach, część I, Statystyka opisowa, WNT, 2002.
2. T. Górecki, Podstawy statystyki z przykładami w R, Wydawnictwo BTC, Legionowo 2011.
3. T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning, Springer, 2009.
4. M. Sobczyk, Statystyka, Wydawnictwo Naukowe PWN, Warszawa 1996.
5. A. Zeliaś, Metody statystyczne, Polskie Wydawnictwo Ekonomiczne, Warszawa 2000.
1. J. Koronacki, J. Mielniczuk, Statystyka dla studentów kierunków technicznych i przyrodniczych, WNT, Warszawa 2001.
Modified by dr Alina Szelecka (last modification: 03-07-2019 12:06)
