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Course info
KET / SAED
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Course description
Department/Unit / Abbreviation
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KET
/
SAED
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Seminar of experimental data analysis
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Form of course completion
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Pre-Exam Credit
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Form of course completion
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Pre-Exam Credit
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Accredited / Credits
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Yes,
3
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
1
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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NO
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Language of instruction
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Czech, English
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Occ/max
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|
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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NO
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Winter semester
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13 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech, English
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
S|N |
Periodicity |
každý rok
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Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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To apprise with the tools for statistical data analysis and to understand principles of these tools.
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Requirements on student
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Course credit requirements:
- participation on lectures and tutorials
- proactive participation on tutorials
- elaboration of semestral work
- successful defense of semestral work and demonstration of knowledge in the range of lectures
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Content
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Lectures:
1) Descriptive statistics, point and interval estimates, missing values and outliers, determining the sample size
2) Testing data assumptions, statistical hypothesis testing, normality testing
3) Analysis of variance
4) Analysis of variance, correlation and regression analysis
5) Time series analysis
6) Use of neural networks for data analysis
7) Experimental work, models of experiments, phases of experiments
Tutorials:
1) Introduction, descriptive statistics
2) Descriptive statistics, point and interval estimates
3) Testing of outliers and extremes
4) Testing data assumptions, statistical hypothesis testing
5) Statistical hypothesis testing
6) Testing normality and other distributions
7) Analysis of variance - 1 factor, 2 factors without interactions
8) Analysis of variance - 2 factors with interactions, multifactor ANOVA
9) Regression analysis, regression models
10) Time series analysis
11) Interpolation, approximation
12) Neural networks
13) Defense of semestral work, credit
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Activities
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Fields of study
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Guarantors and lecturers
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-
Guarantors:
Ing. Lukáš Kupka, Ph.D. ,
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Lecturer:
Ing. Lukáš Kupka, Ph.D. (50%),
Doc. Ing. Olga Tůmová, CSc. (50%),
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Tutorial lecturer:
Ing. Lukáš Kupka, Ph.D. (70%),
Ing. Petr Netolický, Ph.D. (30%),
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Literature
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Recommended:
Montgomery, Douglas C. Design and analysis of experiments : international student version. 7th ed. Hoboken : John Wiley & Sons, 2009. ISBN 978-0-470-39882-1.
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Recommended:
Tůmová, Olga. Metrologie a hodnocení procesů. 1. vyd. Praha : BEN - technická literatura, 2009. ISBN 978-80-7300-249-7.
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Recommended:
Dupač, Václav; Hušková, Marie. Pravděpodobnost a matematická statistika. 2., upr. vyd. Praha : Karolinum, 2013. ISBN 978-80-246-2208-8.
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Recommended:
Meloun, Milan; Militký, Jiří. Statistická analýza experimentálních dat. Vyd. 2., upr. rozš. Praha : Academia, 2004. ISBN 80-200-1254-0.
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Recommended:
FRIEDRICH, V. Statistika II - vysokoškolská učebnice. Plzeň : ZČU, 2002.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Graduate study programme term essay (40-50)
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40
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Contact hours
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39
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Total
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79
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Prerequisites
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Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
apply basic knowledges from probability and statistics |
solve simple examples from the area of probability and statistics |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
use terms, terminology and strategy in experimental work |
design and plan the experiment |
Skills - skills resulting from the course: |
realize a statistical analysis of the data |
realize time series analysis |
use artificial neural networks to evaluate measured data |
Competences - competences resulting from the course: |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Oral exam |
Skills - skills achieved by taking this course are verified by the following means: |
Seminar work |
Oral exam |
Competences - competence achieved by taking this course are verified by the following means: |
Oral exam |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Skills - the following training methods are used to achieve the required skills: |
Seminar |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
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