Course Details
Models of regression
Academic Year 2024/25
DA64 course is part of 7 study plans
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.
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
Institute
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.
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)
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)
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)
Offered to foreign students
Not to offer
Course on BUT site
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.