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Course detail

Models of regression

Course unit code: FAST-DA64
Academic year: 2017/2018
Type of course unit: optional
Level of course unit: Doctoral (3rd cycle)
Year of study: 2
Semester: winter
Number of ECTS credits: 10 
Learning outcomes of the course unit:
Not applicable.
Mode of delivery:
20 % face-to-face, 80 % distance learning
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.
Co-requisites:
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
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.
Recommended or required reading:
ANDĚL, J.: Matematická statistika. SNTL/ALFA Praha 1978
HEBÁK, P., HUSTOPECKÝ, J., MALÁ, I: Vícerozměrné statistické metody 1,2.3. INFORMATORIUM PRAHA 2007
ANDĚL, J.: Statistické metody. MATFYZPRESS PRAHA 2007
MELOUN, M., MILITKÝ, J.: Statistické zpracování experimentálních dat. PLUS Praha 1994
WALPOLE, R.E., MYERS, R.H.: Probability and Statistics for Engineers and Scientists. Macmillan Publishing Company New York 1990
Planned learning activities and teaching methods:
Not applicable.
Assesment methods and criteria linked to learning outcomes:
Not applicable.
Language of instruction:
Czech
Work placements:
Not applicable.
Course curriculum:
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.
Aims:
To provide the students with knowledge needed for sophisticated applications of statistical methods.
Specification of controlled education, way of implementation and compensation for absences:
Extent and forms are specified by guarantor’s regulation updated for every academic year.

Type of course unit:

Lecture: 39 hours, optionally
Teacher / Lecturer: RNDr. Helena Koutková, CSc.

The study programmes with the given course