Course detail
Probability and Statistics II
FSI-SP2 Acad. year: 2025/2026 Winter semester
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course-unit credit requirements: active participation in seminars, mastering the subject matter, passing all written exams, and semester assignment acceptance. Preparing and defending a project.
Examination: written form of the exam (50 points) and oral part (50 points): a practical written part (4 tasks related to random vectors, conditional distribution, multivariate normal distribution, regression analysis, correlation analysis, categorical data analysis); theoretical oral part (4 tasks related to basic notions, their properties, sense and practical use, and proofs of two theorems); evaluation according to the total number of points (scoring 0 points for any of 4 practical tasks or whole theoretical part means failing the exam): excellent (90 – 100 points and both proofs), very good (80 – 89 points and both proofs), good (70 – 79 points and one proof), satisfactory (60 – 69 points), sufficient (50 – 59 points), failed (0 – 49 points).
Participation in the exercise is mandatory and the teacher decides on the compensation for absences.
Language of instruction
Czech
Aims
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme B-MAI-P: Mathematical Engineering, Bachelor's
branch ---: no specialisation, 4 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Syllabus
Random vector, moment characteristics.
Conditional distribution.
Characteristic function.
Multivariate normal distribution – properties.
Distribution of quadratic forms.
Linear regression model (LRM) and parameter estimates in LRM.
Testing hypotheses concerning linear regression model.
Special cases of LRM (regression line, regression parabola, polynomial regression, ANOVA models).
Weighted regression, an introduction to regression diagnostic and linearized regression model.
Correlation analysis
Goodness of fit tests with known and unknown parameters
Introduction to categorical data analysis (chi-square test, measures of association, Fisher factorial test).
Computer-assisted exercise
26 hours, compulsory
Syllabus
Random vector, variance-covariance matrix, correlation matrix.
Conditional distribution, conditional expectation, conditional variance.
Characteristic function – examples, properties.
Properties of the multivariate normal distribution, linear transformation.
Distributions of quadratic forms – examples for normal distribution.
Point and interval estimates of coefficients, variance and values of linear regression function. Statistical software on PC
Testing hypotheses concerning linear regression functions: particular and simultaneous tests of coefficients, tests of model.
Multidimensional linear and nonlinear regression functions and diagnostics on PC.
Correlation coefficients, partial and multiple correlations.
Goodness of fit tests on PC.
Analysis of categorical data: contingency table, chi-square test, Fisher test.