Course Details

Models of regression

DA64 course is part of 22 study plans

Ph.D. full-t. program nD > PST compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nD > FMI compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nD > KDS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nD > MGS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nD > VHS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDK > PST compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDK > KDS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDK > VHS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDK > FMI compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDK > MGS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nDA > PST compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nDA > FMI compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nDA > KDS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nDA > MGS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program nDA > VHS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDKA > PST compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDKA > FMI compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDKA > KDS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDKA > MGS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program nDKA > VHS compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. full-t. program I > GAK compulsory-elective Winter Semester 2nd year 10 credits

Ph.D. combi. program IK > GAK compulsory-elective Winter Semester 2nd year 10 credits

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.

Course Guarantor

RNDr. Helena Koutková, CSc.

Institute

Institute of Mathematics and Descriptive Geometry

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.

Planned educational 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.

Objective

To provide the students with knowledge needed for sophisticated applications of statistical methods.

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

Vymezení kontrolované výuky a způsob jejího provádění stanoví každoročně aktualizovaná vyhláška garanta předmětu.

Lecture

3 hours/week, 13 weeks, elective

Syllabus of lectures

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.