Course: Linear Statistical Models

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Course title Linear Statistical Models
Course code KMA/LSM
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Frequency of the course každý rok
Semester Summer
Number of ECTS credits 6
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Picek Jan, prof. RNDr. CSc.
  • Schindler Martin, Mgr. Ph.D.
Course content
unspecified

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing)
  • Class attendance - 70 hours per semester
  • Preparation for exam - 110 hours per semester
Learning outcomes
The aim is to know the most common types of linear statistical models and to be able to apply them on real data.
Basic knowledge of calculus - functions of several variables, ability to apply advanced methods of mathematical statistics and probability theory
Prerequisites

KMA/PASM

Assessment methods and criteria
Oral exam, Written exam

Recommended literature
  • Anděl, J. Statistické metody. Matfyzpress: Praha, 2007. ISBN 978-80-7378-003-6.
  • Antoch, J., Vorlíčková, D. Vybrané metody statistické analýzy dat, Academia, Praha 1992. Praha: Academia, 1992. ISBN 80-200-0204-9.
  • FARAWAY, J. J. Linear models with R. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2014. ISBN 978-14-3988-733-2.
  • HARRELL, F. E. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Second edition. Heidelberg: Springer, 2015. ISBN 978-80-210-7752-2.
  • KATINA, S., KRÁLÍK, M., HUPKOVÁ, A. Aplikovaná štatistická inferencia I. Brno: Masarykova univerzita, 2015. ISBN 978-80-210-7752-2.
  • ZVÁRA, Karel. Regrese. Praha: Matfyzpres, 2008. ISBN 978-80-7378-041-8.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester