Gurantor department | Department of Applied Mathematics | Credits | 10 |

Subject guarantor | prof. Ing. Radim Briš, CSc. | Subject version guarantor | prof. Ing. Radim Briš, CSc. |

Study level | postgraduate | Requirement | Choice-compulsory type B |

Year | Semester | winter + summer | |

Study language | English | ||

Year of introduction | 2019/2020 | Year of cancellation | |

Intended for the faculties | FEI | Intended for study types | Doctoral |

Instruction secured by | |||
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Login | Name | Tuitor | Teacher giving lectures |

BRI10 | prof. Ing. Radim Briš, CSc. |

Extent of instruction for forms of study | ||
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Form of study | Way of compl. | Extent |

Full-time | Examination | 28+0 |

Part-time | Examination | 28+0 |

The objective of the course is to develop advanced knowledge of statistical tools and procedures, understanding of the advanced theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into his or her own research problems with complex data.

Seminars

Individual consultations

Project work

Other activities

The course will emphasize methods of applied statistics and data analysis. Theoretical considerations will be included to the extent that knowledge of theory is necessary for a sound understanding of methods and contributes to the development of data analysis skills and the ability to interpret results of statistical analysis. Topics are included in the syllabus below.

• BRIŠ, Radim. Probability and Statistics for Engineers. Ostrava, 2011. Available at: http://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf
• JOHNSON, James L. Probability and statistics for computer science. Hoboken, NJ: Wiley Interscience, 2008. ISBN 978-0470383421.
• VAN BELLE, Gerald a Lloyd FISHER. Biostatistics: a methodology for the health sciences. 2nd ed. Hoboken, NJ: John Wiley, 2004. ISBN 0471031852.

• HASTIE, Trevor, Robert TIBSHIRANI a J. H FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, 2009. ISBN 9780387848570.
• JAMES, Gareth, Daniela WITTEN, Trevor HASTIE a Robert TIBSHIRANI. An introduction to statistical learning: with applications in R. New York: Springer, [2013]. Springer texts in statistics, 103. ISBN 978-1-4614-7138-7.
• MOORE, Dirk F. Applied survival analysis using R. New York, NY: Springer Science+Business Media, 2016. ISBN 978-3319312439.
• TUTZ, Gerhard. Regression for categorical data. New York: Cambridge University Press, 2012. ISBN 9781107009653.
• HOSMER, David W a Stanley LEMESHOW. Applied logistic regression. 2nd ed. New York: Wiley, 2000. ISBN 978-0471-35632-8.
• MÜLLER, Peter, Fernando Andres QUINTANA, Alejandro JARA a Tim HANSON. Bayesian Nonparametric Data Analysis. Springer, 2015. ISBN 978-3319189673.

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.

https://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf

The student prepares and presents the work on a given topic.

Subject has no prerequisities.

Subject has no co-requisities.

• Biostatistical Design of Medical Study (Various Types of Studies, Steps Necessary to Perform a Study, Ethics, Data Collection)
• Software Tools for Statistical Computing
• Exploratory Data Analysis (Types of Variables, Summarization and Visualization of Distributions)
• Rudiments of Probability Theory (Working with Probability, Medical Tests and Bayes Theorem, Random Variables and Probability Distribution, Characteristics of Random Variable – Expected Value, Dispersion, …)
• Discrete and Continuous Data Models
• Population and Sample, Sampling Distribution
• Theory of Estimation (Point and Interval Estimation, Maximum Likelihood Estimation Method, Bayesian Inference)
• Hypothesis Testing (Basic of Hypothesis Testing, Type I and Type II Error, p-value, One- and Two-Sample Parametric Tests, Paired Tests, Sample Size Determination)
• One-Way Analysis of Variance (ANOVA, Validity of ANOVA Models, Kruskal-Wallis test, Multiple Comparisons)
• Linear Regression Models with One Predictor Variable
• Linear Regression Models with Multiple Predictor Variables
• Logistic Regression
• Basics of Survival Analysis (Kaplan-Meier Estimate of the Survival Curve, Log-Rank Test, Cox Proportional Hazard Regression Model)
• Stochastic processes (Markov chains, Markov models)

Task name | Type of task | Max. number of points
(act. for subtasks) | Min. number of points |
---|---|---|---|

Examination | Examination |

Show history

Academic year | Programme | Field of study | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
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2020/2021 | (P0588D140004) Bioinformatics and Computational Biology | P | English | Ostrava | Choice-compulsory type B | study plan | ||||||

2020/2021 | (P0588D140004) Bioinformatics and Computational Biology | K | English | Ostrava | Choice-compulsory type B | study plan |

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