Course Details
Models of regression
Academic Year 2023/24
DAB037 course is part of 20 study plans
DPC-V Winter Semester 2nd year
DPC-S Winter Semester 2nd year
DPC-M Winter Semester 2nd year
DPC-K Winter Semester 2nd year
DPC-E Winter Semester 2nd year
DPA-V Winter Semester 2nd year
DPA-S Winter Semester 2nd year
DPA-M Winter Semester 2nd year
DPA-K Winter Semester 2nd year
DPA-E Winter Semester 2nd year
DKC-V Winter Semester 2nd year
DKC-S Winter Semester 2nd year
DKC-M Winter Semester 2nd year
DKC-K Winter Semester 2nd year
DKC-E Winter Semester 2nd year
DKA-V Winter Semester 2nd year
DKA-S Winter Semester 2nd year
DKA-M Winter Semester 2nd year
DKA-K Winter Semester 2nd year
DKA-E 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 systems for regression analysis.
Course Guarantor
Institute
Objective
Syllabus
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.
Prerequisites
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.
Language of instruction
Czech
Credits
10 credits
Semester
Forms and criteria of assessment
Specification of controlled instruction, the form of instruction, and the form of compensation of the absences
Offered to foreign students
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.
6.–7. General linear regression model.
8. Singular linear regression model.
9.–10. Analysis of variance.
11.–12.Factor analysis.
13. Nonlinear regression model.