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Course detail
FSI-SP2Acad. year: 2024/2025
This course is concerned with the following topics: multidimensional normal distribution, linear regression model (estimates, tests of hypotheses, regression diagnostics), nonlinear regression model, introduction to ANOVA, correlation analysis, basic methods of categorical analysis. Students learn about the applicability of those methods and available software for computations.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
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 (70 points) and oral part (30 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).
Aims
Students acquire needed knowledge from important parts of the probability theory and mathematical statistics, which will enable them to evaluate and develop stochastic models of technical phenomena and processes based on these methods and apply them on PC.
Study aids
Prerequisites and corequisites
Basic literature
Recommended literature
Classification of course in study plans
specialization CZS , 1 year of study, winter semester, elective
Lecture
Teacher / Lecturer
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 analysisGoodness of fit tests with known and unknown parametersIntroduction to categorical data analysis (chi-square test, measures of association, Fisher factorial test).
Computer-assisted exercise
E-learning texts