Course detail

Models of regression

FAST-DAB037Acad. year: 2023/2024

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.

Language of instruction

Czech

Number of ECTS credits

10

Mode of study

Not applicable.

Guarantor

Ing. Jan Holešovský, Ph.D.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

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.

Rules for evaluation and completion of the course

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Aims

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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

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)

Classification of course in study plans

  • Programme DKA-S Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPC-S Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKC-S Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPA-S Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPC-E Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPA-E Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKC-E Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKA-E Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKC-M Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKA-M Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPA-M Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPC-M Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPA-K Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKC-K Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPC-K Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKA-K Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPC-V Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKA-V Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DKC-V Doctoral, 2. year of study, winter semester, compulsory-optional
  • Programme DPA-V Doctoral, 2. year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Ing. Jan Holešovský, Ph.D.

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.