Course detail

# Models of regression

FAST-DA64Acad. year: 2018/2019

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.

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.

Recommended optional programme components

Not applicable.

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

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

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.

Classification of course in study plans

• Programme D-P-C-SI (N) Doctoral

branch PST , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-C-SI (N) Doctoral

branch PST , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-E-SI (N) Doctoral

branch PST , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-E-SI (N) Doctoral

branch PST , 2. year of study, winter semester, 10 credits, compulsory-optional
branch MGS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-E-SI (N) Doctoral

branch MGS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-C-SI (N) Doctoral

branch KDS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-C-SI (N) Doctoral

branch KDS , 2. year of study, winter semester, 10 credits, compulsory-optional
branch MGS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-C-SI (N) Doctoral

branch MGS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-C-SI (N) Doctoral

branch FMI , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-E-SI (N) Doctoral

branch FMI , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-E-SI (N) Doctoral

branch FMI , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-C-SI (N) Doctoral

branch FMI , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-E-SI (N) Doctoral

branch KDS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-E-SI (N) Doctoral

branch KDS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-C-SI (N) Doctoral

branch VHS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-E-SI (N) Doctoral

branch VHS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-E-SI (N) Doctoral

branch VHS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-C-SI (N) Doctoral

branch VHS , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-K-C-GK Doctoral

branch GAK , 2. year of study, winter semester, 10 credits, compulsory-optional

• Programme D-P-C-GK Doctoral

branch GAK , 2. year of study, winter semester, 10 credits, compulsory-optional

#### Type of course unit

Lecture

39 hours, optionally

Teacher / Lecturer

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.

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