310-4003/02 – Mathematical statistics and data analysis (MSAD)

Gurantor departmentDepartment of Mathematics and Descriptive GeometryCredits10
Subject guarantorMgr. Marcela Rabasová, Ph.D.Subject version guarantorMgr. Marcela Rabasová, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
KUC14 prof. RNDr. Radek Kučera, Ph.D.
KAH14 Mgr. Marcela Rabasová, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

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:

Dummer R. M.: INTRODUCTION TO STATISTICAL SCIENCE. VŠB-TU Ostrava 1998; ISBN 80-7078-497-0

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 ================= 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

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (P2116) Mineral Raw Materials (2102V009) Processing P English Ostrava Choice-compulsory study plan
2019/2020 (P2116) Mineral Raw Materials (2102V009) Processing P English Ostrava Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner