714-0951/03 – Mathematical statistics and data analysis (MSAD)
Gurantor department | Department of Mathematics and Descriptive Geometry | Credits | 10 |
Subject guarantor | Mgr. Marcela Rabasová, Ph.D. | Subject version guarantor | Mgr. Marcela Rabasová, Ph.D. |
Study level | postgraduate | Requirement | Choice-compulsory |
Year | | Semester | winter + summer |
| | Study language | Czech |
Year of introduction | 2013/2014 | Year of cancellation | 2019/2020 |
Intended for the faculties | HGF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide theoretical and practical foundation for understanding the importance of basic probability concepts and teach the student statistical thinking as a way of understanding the processes and events around us and to acquaint him with the basic methods of gathering and analyzing statistical data.
Teaching methods
Individual consultations
Project work
Summary
1. Descriptive statistics - statistical file with one factor, statistical file with two factors, grouped frequency distribution
2. Inductive statistics - random sample, point and interval estimations of parameters, hypothesis testing
3. Regression analysis - least squares approximation, linear regression, nonlinear regression
Compulsory literature:
Recommended literature:
Radim Briš, Petra Škňouřilová. STATISTICS I. VŠB - Technical University of Ostrava, Ostrava 2007.
Way of continuous check of knowledge in the course of semester
Tests and credits
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Teaching is based on individual consultations. Program and the exam questions are analogous to the program of lectures.
E-learning
Other requirements
Requirements are given by the outline of the subject.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Descriptive statistics - statistical file with one factor, statistical file with two factors, grouped frequency distribution
2. Inductive statistics - random sample, point and interval estimations of parameters, hypothesis testing
3. Regression analysis - least squares approximation, linear regression, nonlinear regression
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.