Theory: 50 %

Practice: 50 %

Recommended semester: 3

Study mode: full-time

Prerequisites: Calculus 1

Evaluation type: term mark

Course category: compulsory

Language: english

Responsible instructor: Osztényiné dr. Krauczi Éva

Responsible department: Department of Basic Sciences

Instructor(s): Kelecsényi Klára, - nincs

Course objectives:
The course is an introduction to probability and statistics. The topics covered include descriptive statistics, probability and inferential statistics. The aim of the course is to introduce the notions, methods an the necassary theoretical background related to data analysis and probability with applications in engineering.

Course content - lectures:

1. Descriptive statistics: measures of location 2. Descriptive statistics: variability and shape 3. Mathematical model of random experiments, relative frequency, events and probability, Kolmogorov axioms 4. Classical probability model 5. Conditional probability, independence 6. Discrete random variables, expected value and variance 7. Binomial, Hypergeometric and Poisson distribution 8. Continuous random variables, expected value and variance 9. Uniform, Exponential and Normal Distribution, De Moivre-Laplace theorem, Central limit theorem 10. Sampling distributions, point and interval estimation 11. Hypothesis testing 12. Analysing bivariate data: Khi-square test for independence 13. Analysing bivariate data: correlation, regression

Course content - seminars:

1. Descriptive statistics: measures of location 2. Descriptive statistics: variability and shape 3. Mathematical model of random experiments, relative frequency, events and probability, Kolmogorov axioms 4. Classical probability model 5. Conditional probability, independence 6. Discrete random variables, expected value and variance 7. Binomial, Hypergeometric and Poisson distribution 8. Continuous random variables, expected value and variance 9. Uniform, Exponential and Normal Distribution, De Moivre-Laplace theorem, Central limit theorem 10. Sampling distributions, point and interval estimation 11. Hypothesis testing 12. Analysing bivariate data: Khi-square test for independence 13. Analysing bivariate data: correlation, regression

Acquired competences:
Knowledge:

Knowledge of the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of IT.

Skills:

Attitude:

He/she makes an effort to work efficiently and to high standards.

Autonomy and responsibilities:

Additional professional competences:

Efficient use of digital technology, knowledge of digital solutions to fulfill educational objectives

Requirements, evaluation, grading:
Mid-term study requirements:
Four midterm tests (4*25 points).
Exam requirements:

Generative AI usage:

1st position: The use of GAI tools is not permitted when solving tasks. This means that GAI tools cannot be used when creating or solving formative or summative assessment elements, and the use of generative AI constitutes academic misconduct. The use of AI tools for language and spelling checking is not subject to the complete ban under the 1st position.

Study aids, laboratory background:

Materials uploaded to TEAMS.

Compulsory readings:

Authors: Barbara Illowsky, Susan Dean Publisher/website: OpenStax Book title: Introductory Statistics 2e Publication date: Dec 13, 2023 Location: Houston, Texas Book URL: https://openstax.org/books/introductory-statistics-2e

Recommended readings:

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