Course Information

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

Mgr. Petra Ráboňová, Ph.D. (lecturer)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Martin Dzúrik (seminar tutor)
Mgr. Barbora Halaštová (seminar tutor)
Mgr. Bc. Martin Chvátal, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)

Guaranteed by

Mgr. Petra Ráboňová, Ph.D.
Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101, except Tue 19. 9., except Tue 7. 11.

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 8:00–9:50 VT314, except Wed 20. 9., except Wed 8. 11., M. Cabalka
BPM_STA1/03: Wed 14:00–15:50 VT314, except Wed 20. 9., except Wed 8. 11., V. Reichel
BPM_STA1/04: Wed 16:00–17:50 VT314, except Wed 20. 9., except Wed 8. 11., V. Reichel
BPM_STA1/05: Thu 8:00–9:50 VT206, except Thu 21. 9., except Thu 9. 11., P. Ráboňová
BPM_STA1/06: Thu 10:00–11:50 VT206, except Thu 21. 9., except Thu 9. 11., M. Cabalka
BPM_STA1/07: Tue 12:00–13:50 VT206, except Tue 19. 9., except Tue 7. 11., P. Ráboňová
BPM_STA1/09: Wed 14:00–15:50 VT202, except Wed 20. 9., except Wed 8. 11., M. Cabalka
BPM_STA1/11: Wed 16:00–17:50 VT202, except Wed 20. 9., except Wed 8. 11., M. Cabalka
BPM_STA1/13: Mon 18:00–19:50 VT202, except Mon 18. 9., except Mon 6. 11., M. Cabalka
BPM_STA1/15: Wed 10:00–11:50 VT314, except Wed 20. 9., except Wed 8. 11., M. Cabalka
BPM_STA1/16: Thu 16:00–17:50 VT204, except Thu 21. 9., except Thu 9. 11., M. Dzúrik
BPM_STA1/18: Thu 10:00–11:50 VT202, except Thu 21. 9., except Thu 9. 11., P. Ráboňová
BPM_STA1/20: Thu 12:00–13:50 VT206, except Thu 21. 9., except Thu 9. 11., M. Cabalka
BPM_STA1/22: Thu 18:00–19:50 VT206, except Thu 21. 9., except Thu 9. 11., M. Dzúrik
BPM_STA1/23: Thu 8:00–9:50 VT204, except Thu 21. 9., except Thu 9. 11., M. Cabalka
BPM_STA1/24: Tue 12:00–13:50 VT202, except Tue 19. 9., except Tue 7. 11., B. Halaštová
BPM_STA1/25: Tue 14:00–15:50 VT202, except Tue 19. 9., except Tue 7. 11., B. Halaštová

Prerequisites (in Czech)

( BPM_VTMA Mathematics Entrance Test )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 13 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Learning outcomes

After graduation of the course student should be able to:
- use and interpret functional and numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context

Syllabus

• 1.Types of variables with respect to measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Basic of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application.
• 9. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• MANN, Prem S. Introductory statistics. Ninth edition. Hoboken: Wiley, 2019, 171 stran. ISBN 9781119148296. info

Teaching methods

Theoretical lectures; practical computer-aided seminar sessions;

Assessment methods

Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

Mgr. Petra Ráboňová, Ph.D. (lecturer)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Martin Dzúrik (seminar tutor)
Mgr. Lenka Franců (seminar tutor)
Mgr. Bc. Martin Chvátal, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Ing. Jana Vechetová (seminar tutor)
Mgr. Lenka Zavadilová, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (assistant)

Guaranteed by

Mgr. Petra Ráboňová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101, except Tue 13. 9., except Tue 1. 11.

• Timetable of Seminar Groups:

BPM_STA1/03: Wed 8:00–9:50 VT314, except Wed 14. 9., except Wed 2. 11., M. Chvátal
BPM_STA1/04: Thu 16:00–17:50 VT202, except Thu 15. 9., except Thu 3. 11., M. Dzúrik
BPM_STA1/06: Wed 14:00–15:50 VT314, except Wed 14. 9., except Wed 2. 11., V. Reichel
BPM_STA1/07: Wed 16:00–17:50 VT314, except Wed 14. 9., except Wed 2. 11., V. Reichel
BPM_STA1/09: Thu 12:00–13:50 VT314, except Thu 15. 9., except Thu 3. 11., M. Chvátal
BPM_STA1/11: Thu 14:00–15:50 VT202, except Thu 15. 9., except Thu 3. 11., M. Dzúrik
BPM_STA1/13: Tue 12:00–13:50 VT206, except Tue 13. 9., except Tue 1. 11., M. Cabalka
BPM_STA1/14: Tue 14:00–15:50 VT202, except Tue 13. 9., except Tue 1. 11., P. Ráboňová
BPM_STA1/17: Wed 14:00–15:50 VT202, except Wed 14. 9., except Wed 2. 11., M. Cabalka
BPM_STA1/19: Tue 16:00–17:50 VT202, except Tue 13. 9., except Tue 1. 11., M. Cabalka
BPM_STA1/20: Wed 16:00–17:50 VT202, except Wed 14. 9., except Wed 2. 11., M. Cabalka
BPM_STA1/23: Tue 12:00–13:50 VT202, except Tue 13. 9., except Tue 1. 11., P. Ráboňová
BPM_STA1/24: Mon 18:00–19:50 VT202, except Mon 12. 9., except Mon 31. 10., M. Cabalka
BPM_STA1/26: Fri 8:00–9:50 VT206, except Fri 16. 9., except Fri 4. 11., M. Dzúrik

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 13 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Learning outcomes

After graduation of the course student should be able to:
- use and interpret functional and numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context

Syllabus

• 1.Types of variables with respect to measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Basic of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application.
• 9. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017, 763, 73. ISBN 9781292099729. info

Teaching methods

Theoretical lectures; practical computer-aided seminar sessions;

Assessment methods

Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Lenka Franců (seminar tutor)
Mgr. Bc. Martin Chvátal, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Ing. Jana Vechetová (seminar tutor)
Mgr. Lenka Zavadilová, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (assistant)
Mgr. et Mgr. Iva Raclavská, DiS. (assistant)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101, except Tue 14. 9., except Tue 2. 11.

• Timetable of Seminar Groups:

BPM_STA1/01: Tue 12:00–13:50 VT105, except Tue 14. 9., except Tue 2. 11., M. Chvátal
BPM_STA1/02: Thu 8:00–9:50 VT204, except Thu 16. 9., except Thu 4. 11., M. Matulová
BPM_STA1/03: Wed 8:00–9:50 VT105, except Wed 15. 9., except Wed 3. 11., M. Chvátal
BPM_STA1/04: Thu 16:00–17:50 VT202, except Thu 16. 9., except Thu 4. 11., M. Matulová
BPM_STA1/05: Wed 12:00–13:50 VT105, except Wed 15. 9., except Wed 3. 11., M. Chvátal
BPM_STA1/06: Wed 14:00–15:50 VT105, except Wed 15. 9., except Wed 3. 11., M. Cabalka
BPM_STA1/07: Wed 16:00–17:50 VT105, except Wed 15. 9., except Wed 3. 11., M. Cabalka
BPM_STA1/08: Wed 18:00–19:50 VT105, except Wed 15. 9., except Wed 3. 11., M. Cabalka
BPM_STA1/09: Thu 12:00–13:50 VT105, except Thu 16. 9., except Thu 4. 11., P. Ráboňová
BPM_STA1/10: Thu 18:00–19:50 VT105, except Thu 16. 9., except Thu 4. 11., P. Ráboňová
BPM_STA1/11: Thu 14:00–15:50 VT202, except Thu 16. 9., except Thu 4. 11., P. Ráboňová
BPM_STA1/12: Thu 16:00–17:50 VT105, except Thu 16. 9., except Thu 4. 11., P. Ráboňová
BPM_STA1/13: Tue 12:00–13:50 VT206, except Tue 14. 9., except Tue 2. 11., J. Böhm
BPM_STA1/14: Tue 14:00–15:50 VT202, except Tue 14. 9., except Tue 2. 11., J. Böhm
BPM_STA1/15: Thu 18:00–19:50 VT206, except Thu 16. 9., except Thu 4. 11.
BPM_STA1/16: Wed 18:00–19:50 VT202, except Wed 15. 9., except Wed 3. 11., J. Böhm
BPM_STA1/17: Wed 14:00–15:50 VT202, except Wed 15. 9., except Wed 3. 11., M. Chvátal
BPM_STA1/18: Wed 16:00–17:50 P104, except Wed 15. 9., except Wed 3. 11., J. Böhm
BPM_STA1/19: Tue 16:00–17:50 VT202, except Tue 14. 9., except Tue 2. 11.
BPM_STA1/20: Wed 16:00–17:50 VT202, except Wed 15. 9., except Wed 3. 11., M. Chvátal

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 22 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Learning outcomes

After graduation of the course student should be able to:
- use and interpret functional and numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context

Syllabus

• 1.Types of variables with respect to measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Basic of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application.
• 9. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017, 763, 73. ISBN 9781292099729. info

Teaching methods

Theoretical lectures; practical computer-aided seminar sessions;

Assessment methods

Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Monika Filová (seminar tutor)
Mgr. Bc. Martin Chvátal, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Lenka Zavadilová, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (assistant)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Tue 12:00–13:50 VT105, M. Chvátal
BPM_STA1/02: Thu 8:00–9:50 VT204, T. Černá
BPM_STA1/03: Wed 8:00–9:50 VT105, T. Černá
BPM_STA1/04: Thu 16:00–17:50 VT202, M. Matulová
BPM_STA1/05: Wed 12:00–13:50 VT105, T. Černá
BPM_STA1/06: Wed 14:00–15:50 VT105, J. Böhm
BPM_STA1/07: Wed 16:00–17:50 VT105, M. Cabalka
BPM_STA1/08: Wed 18:00–19:50 VT105, M. Cabalka
BPM_STA1/09: Thu 12:00–13:50 VT105, T. Černá
BPM_STA1/10: Thu 18:00–19:50 VT105, M. Chvátal
BPM_STA1/11: Thu 14:00–15:50 VT202, M. Matulová
BPM_STA1/12: Thu 16:00–17:50 VT105, M. Chvátal
BPM_STA1/13: Tue 12:00–13:50 VT206, J. Böhm
BPM_STA1/14: Tue 14:00–15:50 VT202, J. Böhm
BPM_STA1/15: Thu 18:00–19:50 VT206
BPM_STA1/16: Wed 18:00–19:50 VT202, J. Böhm
BPM_STA1/17: Wed 14:00–15:50 VT202, M. Chvátal
BPM_STA1/18: Wed 16:00–17:50 P104, M. Chvátal

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 22 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Learning outcomes

After graduation of the course student should be able to:
- use and interpret functional and numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context

Syllabus

• 1.Types of variables with respect to measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Basic of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application.
• 9. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017, 763, 73. ISBN 9781292099729. info

Teaching methods

Theoretical lectures; practical computer-aided seminar sessions;

Assessment methods

Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at progress test
4. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Monika Filová (seminar tutor)
Lenka Hráčková (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (assistant)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Tue 12:00–13:50 VT105, M. Králová
BPM_STA1/02: Thu 8:00–9:50 VT204, M. Matulová
BPM_STA1/03: Wed 8:00–9:50 VT105, J. Böhm
BPM_STA1/04: Thu 16:00–17:50 VT202, P. Ráboňová
BPM_STA1/05: Wed 12:00–13:50 VT105, J. Böhm
BPM_STA1/06: Wed 14:00–15:50 VT105, J. Böhm
BPM_STA1/07: Wed 16:00–17:50 VT105, T. Černá
BPM_STA1/08: Wed 18:00–19:50 VT105, T. Černá
BPM_STA1/09: Thu 12:00–13:50 VT105, P. Ráboňová
BPM_STA1/10: Thu 18:00–19:50 VT105, P. Ráboňová
BPM_STA1/11: Thu 14:00–15:50 VT202, P. Ráboňová
BPM_STA1/12: Thu 16:00–17:50 VT105, M. Matulová
BPM_STA1/13: Tue 12:00–13:50 VT206, M. Filová
BPM_STA1/14: Tue 14:00–15:50 VT202, M. Filová
BPM_STA1/15: Thu 18:00–19:50 VT206, T. Černá
BPM_STA1/17: Wed 18:00–19:50 VT202, J. Böhm

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 22 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Learning outcomes

After graduation of the course student should be able to:
- use and interpret functional and numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context

Syllabus

• 1.Types of variables with respect to measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Basic of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application.
• 9. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017, 763, 73. ISBN 9781292099729. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info

Teaching methods

Theoretical lectures; practical computer-aided seminar sessions;

Assessment methods

Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at progress test
4. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Štěpán Křehlík, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 10:00–11:50 P101

• Timetable of Seminar Groups:

BPM_STA1/T01: Tue 18. 9. to Thu 13. 12. Tue 10:00–11:50 117, Thu 20. 9. to Thu 13. 12. Thu 14:00–15:50 108, E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA1/01: Wed 16:00–17:50 P103, T. Černá
BPM_STA1/02: Wed 10:00–11:50 S315, J. Böhm
BPM_STA1/03: Wed 18:00–19:50 P103, T. Černá
BPM_STA1/04: Thu 16:00–17:50 P201, P. Ráboňová
BPM_STA1/05: Wed 12:00–13:50 P303, J. Böhm
BPM_STA1/07: Thu 14:00–15:50 P201, P. Ráboňová
BPM_STA1/08: Wed 18:00–19:50 P106, P. Ráboňová
BPM_STA1/09: Thu 12:00–13:50 P104, P. Ráboňová
BPM_STA1/10: Wed 8:00–9:50 P201, J. Böhm
BPM_STA1/11: Thu 8:00–9:50 P103, M. Matulová
BPM_STA1/12: Tue 12:00–13:50 P201, M. Králová
BPM_STA1/13: Thu 10:00–11:50 P303, J. Böhm
BPM_STA1/14: Wed 12:00–13:50 P102, Š. Křehlík

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 16 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Štěpán Křehlík, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. et Mgr. Alena Novotná (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 16:20–17:55 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 16:20–17:55 P103, O. Černý
BPM_STA1/02: Tue 11:05–12:45 S311, J. Böhm
BPM_STA1/03: Wed 18:00–19:35 P103, O. Černý
BPM_STA1/04: Thu 16:20–17:55 P201, Š. Křehlík
BPM_STA1/05: Tue 9:20–11:00 P106, J. Böhm
BPM_STA1/07: Wed 14:35–16:15 P304, J. Böhm
BPM_STA1/10: Thu 14:35–16:15 P201, T. Zdražil
BPM_STA1/11: Wed 18:00–19:35 P106, V. Reichel
BPM_STA1/13: Thu 12:50–14:30 P104, T. Zdražil
BPM_STA1/15: Wed 9:20–11:00 P201, J. Böhm
BPM_STA1/20: Thu 9:20–11:00 P103, Š. Křehlík
BPM_STA1/21: Mon 18:00–19:35 P201, M. Králová
BPM_STA1/23: Thu 11:05–12:45 P303, T. Zdražil

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 16 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

required literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

recommended literature
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Štěpán Křehlík, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. et Mgr. Alena Novotná (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 16:20–17:55 P101

• Timetable of Seminar Groups:

BPM_STA1/T01: Tue 20. 9. to Thu 22. 12. Tue 8:00–9:35 115, V. Reichel, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA1/T02: Tue 20. 9. to Thu 22. 12. Tue 8:00–9:35 117, Thu 22. 9. to Thu 22. 12. Thu 9:40–11:15 108, E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA1/01: Wed 16:20–17:55 P103, O. Černý
BPM_STA1/02: Tue 11:05–12:45 S311, P. Ráboňová
BPM_STA1/03: Wed 18:00–19:35 P103, O. Černý
BPM_STA1/05: Tue 9:20–11:00 P106, P. Ráboňová
BPM_STA1/07: Wed 14:35–16:15 P304, P. Ráboňová
BPM_STA1/10: Thu 14:35–16:15 P201, Š. Křehlík
BPM_STA1/11: Wed 18:00–19:35 P106, V. Reichel
BPM_STA1/13: Thu 12:50–14:30 P104, T. Zdražil
BPM_STA1/14: No timetable has been entered into IS. P. Ráboňová
BPM_STA1/15: Wed 9:20–11:00 P201, P. Ráboňová
BPM_STA1/20: Thu 9:20–11:00 P103, Š. Křehlík
BPM_STA1/21: Mon 18:00–19:35 P201, M. Králová
BPM_STA1/23: Thu 11:05–12:45 P303, T. Zdražil

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 15 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Information about innovation of course.

This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. et Mgr. Alena Novotná (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 16:20–17:55 P101

• Timetable of Seminar Groups:

BPM_STA1/T01: Wed 23. 9. to Tue 22. 12. Wed 8:00–9:35 118, E. Janoušková, A. Novotná
BPM_STA1/01: Wed 16:20–17:55 P103, O. Černý
BPM_STA1/02: Tue 11:05–12:45 S311, P. Ráboňová
BPM_STA1/05: Tue 9:20–11:00 P106, P. Ráboňová
BPM_STA1/07: Wed 14:35–16:15 P304, P. Ráboňová
BPM_STA1/08: Thu 14:35–16:15 VT204, M. Matulová
BPM_STA1/10: Thu 12:50–14:30 VT204, M. Matulová
BPM_STA1/11: Wed 18:00–19:35 P312, O. Černý
BPM_STA1/13: Thu 12:50–14:30 P104, T. Zdražil
BPM_STA1/14: No timetable has been entered into IS. P. Ráboňová
BPM_STA1/15: Wed 12:50–14:30 P104, P. Ráboňová
BPM_STA1/20: Thu 9:20–11:00 VT204, M. Matulová
BPM_STA1/21: Mon 18:00–19:35 P201, M. Králová
BPM_STA1/23: Thu 11:05–12:45 P303, T. Zdražil

Prerequisites (in Czech)

( BPM_MATE Mathematics )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 15 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.

Information about innovation of course.

This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Stanislav Abaffy (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 9:20–11:00 P101, Mon 9:20–11:00 P102

• Timetable of Seminar Groups:

BPM_STA1/T01: Mon 15. 9. to Fri 19. 12. Mon 14:40–16:15 Učebna S7 (18), E. Janoušková
BPM_STA1/01: Wed 16:20–17:55 P103, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/02: Tue 12:00–13:35 S311, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/03: Fri 15:30–17:05 P304, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/04: Fri 11:05–12:45 P102, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/05: Tue 9:20–11:00 P106, S. Abaffy
BPM_STA1/06: Fri 12:50–14:30 P102, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/07: Wed 14:35–16:15 P304, O. Černý
BPM_STA1/08: Thu 14:35–16:15 P106, M. Matulová
BPM_STA1/09: Thu 11:05–12:45 P201, S. Abaffy
BPM_STA1/10: Thu 12:50–14:30 P106, M. Matulová
BPM_STA1/11: Wed 18:00–19:35 P312, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/12: Thu 7:40–9:15 P103, S. Abaffy
BPM_STA1/13: Thu 12:50–14:30 P104, T. Zdražil
BPM_STA1/14: No timetable has been entered into IS. S. Abaffy
BPM_STA1/15: Wed 12:50–14:30 P104, O. Černý
BPM_STA1/16: No timetable has been entered into IS., Tato skupina bude otevřena jen v případě naplnění všech ostatních
BPM_STA1/17: Mon 16:20–17:55 P104, M. Králová
BPM_STA1/18: Fri 12:50–14:30 P304, Tato skupina bude otevřena jen v případě naplnění všech ostatních
BPM_STA1/19: Thu 14:35–16:15 P312, T. Zdražil
BPM_STA1/20: Thu 9:20–11:00 P103, S. Abaffy
BPM_STA1/21: Mon 18:00–19:35 P201, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/22: Wed 11:05–12:45 P104, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.
BPM_STA1/23: Thu 11:05–12:45 P303, T. Zdražil
BPM_STA1/24: Thu 16:20–17:55 S310, Tato skupina bude otevřena jen v případě naplnění všech ostatních skupin.

Prerequisites (in Czech)

( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 15 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Information about innovation of course.

This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Stanislav Abaffy (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 9:20–11:00 P102, Mon 9:20–11:00 P101

• Timetable of Seminar Groups:

BPM_STA1/T01: Fri 20. 9. to Fri 20. 12. Fri 10:00–11:55 Učebna S9 (55), E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA1/T01A: No timetable has been entered into IS.
BPM_STA1/T01AA: No timetable has been entered into IS.
BPM_STA1/T02: Wed 25. 9. to Fri 20. 12. Wed 8:00–9:55 Učebna S10 (56), E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA1/01: Wed 16:20–17:55 P312
BPM_STA1/02: Tue 12:00–13:35 S311
BPM_STA1/03: Mon 11:05–12:45 P104
BPM_STA1/04: Fri 11:05–12:45 P102
BPM_STA1/05: Tue 9:20–11:00 P106, S. Abaffy
BPM_STA1/06: Fri 12:50–14:30 P102
BPM_STA1/07: Wed 14:35–16:15 P304, O. Černý
BPM_STA1/08: Thu 14:35–16:15 P106, M. Matulová
BPM_STA1/09: Thu 11:05–12:45 P201, S. Abaffy
BPM_STA1/10: Thu 12:50–14:30 P106, M. Matulová
BPM_STA1/11: Wed 18:00–19:35 P312
BPM_STA1/12: Thu 7:40–9:15 P103, S. Abaffy
BPM_STA1/13: Thu 12:50–14:30 P104, T. Zdražil
BPM_STA1/14: No timetable has been entered into IS. S. Abaffy
BPM_STA1/15: Wed 12:50–14:30 P104, O. Černý
BPM_STA1/16: No timetable has been entered into IS. M. Králová
BPM_STA1/17: Mon 16:20–17:55 P104, M. Králová
BPM_STA1/18: Fri 12:50–14:30 P304, M. Králová
BPM_STA1/19: Thu 14:35–16:15 P312, T. Zdražil
BPM_STA1/20: Thu 9:20–11:00 P103, S. Abaffy
BPM_STA1/21: Mon 18:00–19:35 P201
BPM_STA1/22: Wed 11:05–12:45 P104
BPM_STA1/23: Thu 11:05–12:45 P303, T. Zdražil
BPM_STA1/24: Thu 16:20–17:55 S310

Prerequisites (in Czech)

( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 14 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Information about innovation of course.

This course has been innovated under the project "Inovace studia ekonomických disciplín v souladu s požadavky znalostní ekonomiky (CZ.1.07/2.2.00/28.0227)" which is cofinanced by the European Social Fond and the national budget of the Czech Republic.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Tomáš Lerch (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Jan Orava (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)
Mgr. Silvie Zlatošová, Ph.D. (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Mon 9:20–11:00 P101, Mon 9:20–11:00 P102

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 16:20–17:55 P312, O. Černý
BPM_STA1/02: Tue 12:00–13:35 S311
BPM_STA1/03: Mon 11:05–12:45 S308
BPM_STA1/04: Fri 11:05–12:45 P102, T. Zdražil
BPM_STA1/05: Tue 8:30–10:05 S311
BPM_STA1/06: Fri 12:50–14:30 P102
BPM_STA1/07: Wed 14:35–16:15 P304, O. Černý
BPM_STA1/08: Thu 14:35–16:15 P106, S. Zlatošová
BPM_STA1/09: Thu 11:05–12:45 P201, T. Zdražil
BPM_STA1/10: Thu 12:50–14:30 P106, S. Zlatošová
BPM_STA1/11: Wed 18:00–19:35 P312
BPM_STA1/12: Thu 7:40–9:15 P103, M. Matulová
BPM_STA1/13: Thu 12:50–14:30 aula Vinařská, T. Zdražil
BPM_STA1/14: No timetable has been entered into IS.
BPM_STA1/15: Wed 12:50–14:30 aula Vinařská
BPM_STA1/16: No timetable has been entered into IS. M. Králová
BPM_STA1/17: Mon 16:20–17:55 aula Vinařská, M. Králová
BPM_STA1/18: Fri 12:50–14:30 P304, M. Králová
BPM_STA1/19: Thu 14:35–16:15 P312, T. Zdražil
BPM_STA1/20: Thu 9:20–11:00 P103, M. Matulová
BPM_STA1/21: Wed 8:30–10:05 S311
BPM_STA1/22: Wed 11:05–12:45 aula Vinařská, M. Matulová
BPM_STA1/23: Thu 11:05–12:45 P303, S. Zlatošová
BPM_STA1/24: Thu 16:20–17:55 S310
BPM_STA1/T01A: No timetable has been entered into IS.
BPM_STA1/T01AA: No timetable has been entered into IS.

Prerequisites (in Czech)

( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 14 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A <90,100); B <80,90); C (<70,80); D <60,70); E <50,60); F <0,50) Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Jan Orava (seminar tutor)
Mgr. Silvie Zlatošová, Ph.D. (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková

Timetable

Mon 9:20–11:00 P102, Mon 9:20–11:00 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 16:20–17:55 P312, T. Lerch
BPM_STA1/02: Tue 12:00–13:35 S311
BPM_STA1/03: Mon 12:50–14:30 S308
BPM_STA1/04: Wed 17:10–18:45 S313
BPM_STA1/05: Tue 8:30–10:05 S311
BPM_STA1/06: Tue 13:45–15:20 S307
BPM_STA1/07: Wed 14:35–16:15 P304, T. Lerch
BPM_STA1/08: Thu 14:35–16:15 P106, J. Orava
BPM_STA1/09: Thu 11:05–12:45 P201, J. Orava
BPM_STA1/10: Thu 12:50–14:30 P106, J. Orava
BPM_STA1/11: Wed 18:00–19:35 P312, T. Lerch
BPM_STA1/12: Thu 7:40–9:15 P103, M. Matulová
BPM_STA1/13: Thu 12:50–14:30 P104, S. Zlatošová
BPM_STA1/14: No timetable has been entered into IS.
BPM_STA1/15: Wed 12:50–14:30 P104, M. Matulová
BPM_STA1/16: No timetable has been entered into IS. M. Králová
BPM_STA1/17: Mon 16:20–17:55 P104, M. Králová
BPM_STA1/18: Fri 13:45–15:20 P304, M. Králová
BPM_STA1/19: Thu 14:35–16:15 P312, S. Zlatošová
BPM_STA1/20: Thu 9:20–11:00 P103, M. Matulová
BPM_STA1/21: Wed 8:30–10:05 S311
BPM_STA1/22: Wed 11:05–12:45 P104, M. Matulová
BPM_STA1/23: Thu 11:05–12:45 P303, M. Matulová
BPM_STA1/24: Thu 16:20–17:55 S310, S. Zlatošová

Prerequisites (in Czech)

( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 21 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

recommended literature
• BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info

not specified
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A (90,100); B (80,89); C (70,79); D (60,69); E (50,59); F (0,49)

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. David Hampel, Ph.D. (seminar tutor)
Mgr. Pavla Krajíčková, Ph.D. (seminar tutor)
doc. Mgr. Maria Králová, Ph.D. (seminar tutor)
Mgr. Tomáš Lerch (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
RNDr. Václav Studený, Ph.D. (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková

Timetable

Mon 9:20–11:00 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 16:20–17:55 P312, T. Lerch
BPM_STA1/02: Tue 12:00–13:35 S311, M. Králová
BPM_STA1/03: Mon 12:50–14:30 VT314, M. Králová
BPM_STA1/04: Wed 16:20–17:55 S305, M. Matulová
BPM_STA1/05: Mon 11:05–12:45 P103, M. Králová
BPM_STA1/06: Tue 13:45–15:20 S307, M. Králová
BPM_STA1/07: Wed 14:35–16:15 P304, T. Lerch
BPM_STA1/08: Thu 14:35–16:15 P106, M. Matulová
BPM_STA1/09: Thu 11:05–12:45 P201, M. Králová
BPM_STA1/10: Thu 12:50–14:30 P106, M. Matulová
BPM_STA1/11: Wed 18:00–19:35 P312, T. Lerch
BPM_STA1/12: Thu 7:40–9:15 P103
BPM_STA1/13: Thu 12:50–14:30 P104, M. Králová
BPM_STA1/14: No timetable has been entered into IS. V. Studený
BPM_STA1/15: Wed 12:50–14:30 P102, V. Studený
BPM_STA1/16: No timetable has been entered into IS. P. Krajíčková
BPM_STA1/17: Mon 16:20–17:55 P104, P. Krajíčková

Prerequisites (in Czech)

( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 15 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

• OSECKÝ, Pavel. Statistické vzorce a věty (Statistical formulas). Druhé rozšířené. Brno (Czech Republic): Masarykova univerzita, Ekonomicko-správní fakulta, 1999, 53 pp. ISBN 80-210-2057-1. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika. 4th ed. Brno: Masarykova univerzita, 2007, 52 pp. ISBN 978-80-210-4246-9. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A (90,100); B (80,89); C (70,79); D (60,69); E (50,59); F (0,49)

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

BPM_STA1 Statistics 1

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: graded credit.

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. David Hampel, Ph.D. (seminar tutor)
Mgr. Pavla Krajíčková, Ph.D. (seminar tutor)
doc. Mgr. Maria Králová, Ph.D. (seminar tutor)
Mgr. Tomáš Lerch (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
RNDr. Václav Studený, Ph.D. (seminar tutor)

Guaranteed by

RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková

Timetable

Mon 9:20–11:00 P101

• Timetable of Seminar Groups:

BPM_STA1/01: Wed 16:20–17:55 P312, T. Lerch
BPM_STA1/02: Tue 12:00–13:35 S311, M. Králová
BPM_STA1/03: Mon 12:50–14:30 VT314, M. Králová
BPM_STA1/04: Wed 16:20–17:55 S305
BPM_STA1/05: Mon 11:05–12:45 P103, M. Králová
BPM_STA1/06: Tue 13:45–15:20 S307, M. Králová
BPM_STA1/07: Wed 14:35–16:15 P304, V. Studený
BPM_STA1/08: Thu 14:35–16:15 P106, M. Matulová
BPM_STA1/09: Thu 11:05–12:45 P201, M. Králová
BPM_STA1/10: Thu 12:50–14:30 P106, M. Matulová
BPM_STA1/11: Wed 18:00–19:35 P312, T. Lerch
BPM_STA1/12: Thu 7:40–9:15 P103, D. Hampel
BPM_STA1/13: Thu 12:50–14:30 P104, M. Králová
BPM_STA1/14: No timetable has been entered into IS. P. Krajíčková
BPM_STA1/15: Wed 12:50–14:30 P102, V. Studený
BPM_STA1/16: No timetable has been entered into IS. P. Krajíčková

Prerequisites (in Czech)

PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 12 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basic terms in calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.

Syllabus

• 1.Frequency and probability, properties of probability, examples.
• 2.Independent events, properties of independent events, sequence of independent events.
• 3. Conditional probability, total probability rule, examples.
• 4. Prior and posterior probabilities, Bayes' theorem, examples.
• 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
• 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
• 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
• 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
• 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
• 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
• 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
• 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
• 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).

Literature

• OSECKÝ, Pavel. Statistické vzorce a věty (Statistical formulas). Druhé rozšířené. Brno (Czech Republic): Masarykova univerzita, Ekonomicko-správní fakulta, 1999, 53 pp. ISBN 80-210-2057-1. info
• HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info
• HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika. 4th ed. Brno: Masarykova univerzita, 2007, 52 pp. ISBN 978-80-210-4246-9. info

Teaching methods

Theoretical lectures; practical seminar sessions;

Assessment methods

Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A (90,100); B (80,89); C (70,79); D (60,69); E (50,59); F (0,49)

Language of instruction

Czech

Follow-Up Courses

• BPM_STA2 Statistics 2

Further comments (probably available only in Czech)

The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  
• MPV_MZVS Research Methods in the Public Sector
  (( BPM_STA1 ) && (!MPV_KMZV))  

• Enrolment Statistics (recent)