Bi7491 Regression Modelling

Faculty of Science
Spring 2022
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
RNDr. Ondřej Májek, Ph.D. (lecturer)
RNDr. Tomáš Pavlík, Ph.D. (lecturer)
Guaranteed by
RNDr. Tomáš Pavlík, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Ondřej Májek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 11:00–13:50 D29/347-RCX2
Prerequisites
Student should be familiar with the following topics: fundamentals of the probability theory; vector and matrix algebra; random variable, its distribution and characteristics; hypothesis testing; linear model.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The objective of this course is to familiarize the students with the topic of biostatistical regression modelling, i.e. finding the relationship between measured outcomes and explanatory variables. The course is practically oriented and includes numerous examples solved in the R software, namely in the areas of clinical biostatistics and epidemiology.
Learning outcomes
At the end of this course, a student should be able to:
define various types of regression models;
design and build up a regression model suitable for the particular problem;
evaluate a fit of the proposed model;
interpret results of the regression analysis;
Syllabus
  • Repetition of relevant biostatistical topics.
  • Concepts in regression modelling: regression modelling strategies, assumptions of linear regression model, independent variables, setup and performance the analysis, interpretation and evaluation of the assumptions.
  • Logistic and Poisson regression models.
  • Mixed model.
  • Non-linear modelling.
  • Validation of the predictive regression model.
Literature
  • HARRELL, Frank E. Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001, xxii, 568. ISBN 1441929185. info
Teaching methods
Lectures focused on theoretical aspects as well as practical applications. Practices in a computer room focused on practical regression modelling in R software.
Assessment methods
There is one short test during the term. Students should work up a project concerning one practical application of regression modelling. The course is finished with both written and oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Doporučeno absolvování předmětu Bi5040 Biostatistika - základní kurz nebo Bi5045 Biostatistika v matematické biologii.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021.
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