UFZP002 Statistics and data analysis

Faculty of Philosophy and Science in Opava
Winter 2019
Extent and Intensity
2/2/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Stanislav Hledík, Ph.D. (lecturer)
Mgr. Adam Hofer (seminar tutor)
Guaranteed by
doc. RNDr. Stanislav Hledík, Ph.D.
Centrum interdisciplinárních studií – Faculty of Philosophy and Science in Opava
Timetable
Thu 14:45–16:20 404
  • Timetable of Seminar Groups:
UFZP002/A: Thu 18:05–19:40 404, A. Hofer
Prerequisites (in Czech)
TYP_STUDIA(B)
Vhodné je absolvování předmětu UF/PA127 "Matematika I".
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The seminar aims to enhance teaching subjects GSM monitoring systems I and II (UF/GSM01, UF/GSM02) and Medical diagnostic systems and medical data processing (UF/BM001). It will address selected methods of statistical processing and analyzing of monitored data. Given the focus of the seminar, the possibility of its wider use at several other fields of study offers. The lectures are supplemented by interactive computer demonstrations based on real data and cases.
Syllabus
  • 1. Introduction to statistical methodology. The content and meaning of statistics, population and sample, surveys, collection and processing of data, analysis and interpretation of results.
    2. Basics of the probability theory. Probability, random experiment, random event, independence, conditional probability, random variable discrete and continuous, probability distribution function (probability density function, PDF) and (cumulative) distribution function (CDF), the basic distribution.
    3. Characteristics of probability distributions and descriptive statistics. Moments, mean, variance, standard deviation, skewness, kurtosis, another measures of variability, median, quantiles, modus, transformations of random variables, methods of graphical presentation.
    4. Basic of theory estimation. Point and interval estimation, unbiased and best unbiased estimate. Asymptotic properties of estimates, consistent estimation. Construction of point and interval estimate.
    5. Hypothesis testing. Methodology, statistical hypothesis, the null and alternative hypothesis, test statistics, the level of statistical significance, p-value, the error of first and second kind.
    6. Selected statistical tests. Parametric: Student's t-test, F-test, goodness-of-fit tests (chi square, Kolmogorov-Smirnov test), dependency analysis (contingency and association tables, Pearson coefficient), analysis of variance (ANOVA), post hoc analysis. Nonparametric: Mann-Whitney test, Kruskal-Wallis test, Spearman's rho, Kendall tau, test for dependent samples (Friedman test).
    7. Regression and correlation analysis. Model, model coefficients, linear regression model, point estimates, generalized linear regression.
    8. ROC analysis. Receiver operating characteristics (ROC) analysis as a tool for evaluation of classification and prediction models and to visualize their performance.
    Current information and additional study materials can be found here: http://j.mp/iaka4/
    (once the page appears, click gradually on the folders Kursy, SUO-FPF, 2-LS, UFSAD01-SeminarStatAnDat).
Literature
    recommended literature
  • Litschmannová M. Úvod do statistiky. VŠB-TU Ostrava, Fakulta elektrotechniky a inform, 2011. URL info
  • Litschmannová M. Vybrané kapitoly z pravděpodobnosti. VŠB-TU Ostrava, Fakulta elektrotechniky a inform, 2011. URL info
    not specified
  • Mangano S. Mathematica Cookbook. Building Blocks for Science, Engineering, Finance, Music, and More. O?Reilly, Sebastopol, 2010, 2010. ISBN 978-0-596-52099-1. info
  • Anděl J. Statistické metody. MatFyzPress, Praha, 2007. ISBN 80-7378-001-1. info
  • Anděl J. Základy matematické statistiky. MatFyzPress, Praha, 2007. ISBN 80-7378-003-8. info
  • Everitt B. S. The Cambridge Dictionary of Statistics. Cambridge University Press, Cambridge, 2006. ISBN 0-521-69027-7. URL info
  • Press W. H., Teukolsky S. A., Vetterling W. T., Flannery B. P. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, 1997. ISBN 0-521-43108-5. URL info
Teaching methods
Lecture with a video analysis
One-to-One tutorial
Seminar classes
Assessment methods
The analysis of student 's performance
Credit
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Teacher's information
The attendance at lectures is recommended. It can be substituted by the self-study of recommended literature and individual consultations. Tutorial attendance is compulsory (min. 80%).
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016, Winter 2017, Winter 2018, Winter 2020, Winter 2021, Winter 2022.
  • Enrolment Statistics (Winter 2019, recent)
  • Permalink: https://is.slu.cz/course/fpf/winter2019/UFZP002