Course: Quantitative data evaluation

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Course title Quantitative data evaluation
Course code KMA/XHKD
Organizational form of instruction Lecture + Lesson
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 8
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Lecturer(s)
  • Mrkvička Tomáš, doc. RNDr. Ph.D.
Course content
1. Random variable 2. Diskrete 3. Continuos 4. Random vector 5. Central limit theorem 6. Data processing 7. Random sample 8. Interval estimate 9. Hypothesis testing 10. Hypothesis testing 11. Comparison of more samples 12. Linear regression 13. Correlation analysis, goodness-of-fit tests 14. Contingency tables

Learning activities and teaching methods
Monologic (reading, lecture, briefing)
Learning outcomes
Random variable, characteristic of random variable, independence, random sample, limit theorems, interval estimators, parametrical tests, CLT, linear regression, ANOVA.
Basic skills in probability and statististics.
Prerequisites
no

Assessment methods and criteria
Written examination

Everything in content of course.
Recommended literature
  • Dupač, V., Hušková, M.:. Pravděpodobnost a matematická statistika. Karolinum, Praha, 1999.
  • Mrkvička T., Tlustý P.:. Úvod do teorie pravděpodobnosti. Jihočeská univerzita, České Budějovice, 2008.
  • Mrkvička T., Petrášková V.:. Úvod do statistiky. Jihočeská univerzita, České Budějovice, 2006.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Education Study plan (Version): Theory of Mathematics Education (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Winter
Faculty: Faculty of Education Study plan (Version): Theory of Mathematics Education (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Winter