M6120 Linear Models in Statistics II

Faculty of Science
Spring 2015
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Kateřina Pokorová, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 12:00–13:50 MP1,01014, M. Forbelská
M6120/02: Wed 8:00–9:50 MP1,01014, M. Forbelská
M6120/03: Tue 12:00–13:50 MP1,01014, M. Forbelská
Prerequisites
M5120 Linear Models in Statistics I
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis. Computer skill: working knowledge of the numerical computing environment R.
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
After passing the course, the student will be able:
to define and interpret the basic notions used in the theory of linear models and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in the theory of linear models;
to apply acquired pieces of knowledge for the solution of specific problems of linear models that are not full rank, especially the analysis of variance including problems of applicative character.
For statistical calculations, students learn during the seminars to use the programming environment R in detail which then, they will be able to use in practice.
Syllabus
  • Regular (full rank) and singular (not of full rank) linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification. Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Participation in seminars (10%), four homework assigments (30%), final oral exam (60%).
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2015, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2015/M6120