Course detail

Models of regression

FAST-DA64Acad. year: 2020/2021

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.

Language of instruction

Czech

Number of ECTS credits

10

Mode of study

Not applicable.

Guarantor

RNDr. Helena Koutková, CSc.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

Learning outcomes of the course unit

Not applicable.

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.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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.

Work placements

Not applicable.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme D-P-E-CE (N) Doctoral

    branch FMI , 2 year of study, winter semester, compulsory-optional
    branch KDS , 2 year of study, winter semester, compulsory-optional
    branch MGS , 2 year of study, winter semester, compulsory-optional
    branch PST , 2 year of study, winter semester, compulsory-optional
    branch VHS , 2 year of study, winter semester, compulsory-optional

  • Programme D-P-C-SI (N) Doctoral

    branch FMI , 2 year of study, winter semester, compulsory-optional
    branch KDS , 2 year of study, winter semester, compulsory-optional
    branch MGS , 2 year of study, winter semester, compulsory-optional
    branch PST , 2 year of study, winter semester, compulsory-optional
    branch VHS , 2 year of study, winter semester, compulsory-optional

  • Programme D-P-C-GK Doctoral

    branch GAK , 2 year of study, winter semester, compulsory-optional

  • Programme D-K-E-CE (N) Doctoral

    branch FMI , 2 year of study, winter semester, compulsory-optional
    branch KDS , 2 year of study, winter semester, compulsory-optional
    branch MGS , 2 year of study, winter semester, compulsory-optional
    branch PST , 2 year of study, winter semester, compulsory-optional
    branch VHS , 2 year of study, winter semester, compulsory-optional

  • Programme D-K-C-SI (N) Doctoral

    branch FMI , 2 year of study, winter semester, compulsory-optional
    branch KDS , 2 year of study, winter semester, compulsory-optional
    branch MGS , 2 year of study, winter semester, compulsory-optional
    branch PST , 2 year of study, winter semester, compulsory-optional
    branch VHS , 2 year of study, winter semester, compulsory-optional

  • Programme D-K-C-GK Doctoral

    branch GAK , 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

RNDr. Helena Koutková, CSc.

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.