Course Details

Models of regression

Academic Year 2024/25

DAB037 course is part of 20 study plans

​ DKA-E Winter Semester 2nd year

​ DKA-K Winter Semester 2nd year

​ DKA-M Winter Semester 2nd year

​ DKA-S Winter Semester 2nd year

​ DKA-V Winter Semester 2nd year

​ DPA-E Winter Semester 2nd year

​ DPA-K Winter Semester 2nd year

​ DPA-M Winter Semester 2nd year

​ DPA-S Winter Semester 2nd year

​ DPA-V Winter Semester 2nd year

​ DKC-E Winter Semester 2nd year

​ DKC-K Winter Semester 2nd year

​ DKC-M Winter Semester 2nd year

​ DKC-S Winter Semester 2nd year

​ DKC-V Winter Semester 2nd year

​ DPC-E Winter Semester 2nd year

​ DPC-K Winter Semester 2nd year

​ DPC-M Winter Semester 2nd year

​ DPC-S Winter Semester 2nd year

​ DPC-V Winter Semester 2nd year

Multidimensional normal distribution, conditional probability distribution. Regression function. Linear regression model. Nonlinear regression model. Analysis of variance. Factor analysis. The use of statistical systems for regression analysis.

Credits

10 credits

Language of instruction

Czech

Semester

winter

Course Guarantor

prof. RNDr. Josef Diblík, DrSc.

Institute

Institute of Mathematics and Descriptive Geometry

Forms and criteria of assessment

examination

Entry Knowledge

Basics of the theory of probability, mathematical statistics and linear algebra - the normal distribution law, numeric characteristics of random variables and vectors and their point and interval estimates, principles of the testing of statistical hypotheses, solving a system of linear equations, inverse to a matrix.

Aims

To provide the students with knowledge needed for sophisticated applications of statistical methods.

Basic Literature

ANDĚL, J.: Základy matematické statistiky. Praha: MatFyzPress, 2007, 358 s. ISBN 80-7378-001-1. (cs)
ANDĚL, J.: Statistické metody. Praha: MatFyzPress, 2007, 299 s. ISBN 80-7378-003-8. (cs)
WALPOLE, R.E., MYERS, R.H. Probability and Statistics for Engineers and Scientists. 8th ed. London: Prentice Hall, Pearson education LTD, 2007. 823 p. ISBN 0-13-204767-5.  (en)

Recommended Reading

CASELLA, G., BERGER, R.L. Statistical Inference. Belmont: Brooks/Cole Cengage Learning, 2002. ISBN-13 978-0-534-24312-8.  (en)
MELOUN, M., MILITKÝ, J.: Statistické zpracování experimentálních dat. Praha: PLUS, 1994, 839 s. ISBN 80-85297-56-6.  (cs)
HEBÁK, P., HUSTOPECKÝ, J. Vícerozměrné statistické metody 1. Praha: Informatorium, 2007. 253 p. ISBN 8-07-3330356-9.  (cs)

Offered to foreign students

Not to offer

Course on BUT site

https://www.vut.cz/en/students/courses/detail/281948

Lecture

13 weeks, 3 hours/week, elective

Syllabus

  • 1. Multidimensional normal distribution, conditional probability distribution.
  • 2. Regression function.
  • 3.–5. Linear regression model.
  • 6.–7. General linear regression model.
  • 8. Singular linear regression model.
  • 9.–10. Analysis of variance.
  • 11.–12.Factor analysis.
  • 13. Nonlinear regression model.