Course Details

Models of regression

Academic Year 2023/24

DA64 course is part of 12 study plans

​ D-P-C-SI (N) / PST Winter Semester 2nd year

​ D-P-C-SI (N) / FMI Winter Semester 2nd year

​ D-P-C-SI (N) / KDS Winter Semester 2nd year

​ D-P-C-SI (N) / MGS Winter Semester 2nd year

​ D-P-C-SI (N) / VHS Winter Semester 2nd year

​ D-K-C-SI (N) / VHS Winter Semester 2nd year

​ D-K-C-SI (N) / MGS Winter Semester 2nd year

​ D-K-C-SI (N) / PST Winter Semester 2nd year

​ D-K-C-SI (N) / FMI Winter Semester 2nd year

​ D-K-C-SI (N) / KDS Winter Semester 2nd year

​ D-K-C-GK / GAK Winter Semester 2nd year

​ D-K-E-SI (N) / PST 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 system STATISTICA and EXCEL for regression analysis.

Credits

10 credits

Language of instruction

Czech

Semester

winter

Course Guarantor

RNDr. Helena Koutková, CSc.

Institute

Institute of Mathematics and Descriptive Geometry

Forms and criteria of assessment

examination

Entry Knowledge

Subjects taught in the course DA03, DA62 - Probability and mathematical statistics
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 s. ISBN 8-07-3330356-9. (cs)

Syllabus

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

Prerequisites

Subjects taught in the course DA03, DA62 - Probability and mathematical statistics
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.

Specification of controlled instruction, the form of instruction, and the form of compensation of the absences

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

Offered to foreign students

Not to offer

Course on BUT site

https://www.vut.cz/en/students/courses/detail/272112

Lecture

13 weeks, 3 hours/week, elective

Syllabus

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