      # Course syllabus ST1 - Statistics (FBE - WS 2019/2020)

ECTS syllabus          Syllabus          Timetable

Czech          English

Course code: ST1
Course title in language of instruction: Statistika
Course title in Czech: Statistics
Course title in English: Statistics
Mode of completion and number of credits: Exam (6 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
part-time, 16/0 (lectures per period / seminars per period)
Language of instruction: Czech
Level of course: bachelor
Semester: WS 2019/2020
Name of lecturer: Mgr. Veronika Blašková, Ph.D. (examiner, instructor, lecturer, supervisor)
Ing. Michaela Staňková (examiner, instructor)
doc. Ing. Luboš Střelec, Ph.D. (examiner, instructor)
Ing. Jakub Šácha, Ph.D. (examiner, instructor, lecturer)
Ing. Terézia Vančová (examiner, instructor)
Prerequisites: Mathematics I and (now Mathematics II or Mathematics II)

Aims of the course:
Acquirement of theoretical knowledge and basic calculation procedures and interpretation in the field of descriptive statistics, probability theory, estimation and inferential statistics (confidence intervals, hypothesis tests, nonparametrics).

Course contents:
1.Introduction to statistics (allowance 8/8)

 a. The term of statistics, methods, therminology, tables and plots, stages of statistical investigation, frequency tables b. statistical data, characteristics of level, variability, skewness and kurtosis

2.probability calculations (allowance 10/12)

 a. elementary terminology of probability and distribution theory, definitions b. repeated trials, with and without replacement c. random variables, probability distribution, distribution function, probability density function, probability mass function d. property of random variables e. important distributions of continuous and discrete random variables

3.Fundamentals of inferential statistics (allowance 10/8)

 a. Probability distributions of some important test statistics b. Population and random sample c. Point estimation, construction and properties d. Confidence intervals, construction and properties e. Statistical hypothesis tests, steps, errors f. Parametric and nonparametric tests

Learning outcomes and competences:
Generic competences:

 - Ability to analyse and synthesize - Ability to apply knowledge - Basic computing skills - Skilled at utilizing and processing information

Specific competences:

 - Statistical properties of univariate data - Student can calculate statistical characteristics, interpret results with applications to data - Students are able to test statistical hypotheses and apply the results in practice - Students are able to work with probabilities and utilize probability and distribution theory in statistical testing. - Students can use graphs to interpret the data

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -

 Type of teaching method Daily attendance Combined form Direct teaching lecture 28 h 16 h practice 24 h 0 h consultation 4 h 0 h Self-study preparation for exam 96 h 131 h preparation for regular assessment 0 h 15 h preparation for regular testing 10 h 0 h elaboration and execution of projects 6 h 6 h Total 168 h 168 h

Assessment methods:
Credit is required for admission to the final exam. During semester, student performance is evaluated upon submission of written project and written midterm exam (max. 40 points can be awarded). In total, a student must receive at least 20 points to obtain the credit. Final exam consists of written and oral part. Computer assisted written exam takes 50 minutes. To pass, student must earn at least 50% total score. Students passing the written part may proceed to the oral exam.