Course: Statistics

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Course title Statistics
Course code SKS/STATK
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study 1
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Lecturer(s)
  • Urbanová Pavla, Mgr.
  • Urban Jan, Ing. Ph.D.
  • Císař Petr, Ing. Ph.D.
Course content
Lectures: 1 - Introduction to the course, basic terminology. Random events. 2 - Random events a their probabilities. 3 - Dependent and independent events, conditional probabilities. 4 - Discrete random variables, distribution function, examples of discrete distribution, using of Excel. 5 - Continuous random variables and their properties, quantiles. 6 - Descriptive statistics. 7 - Descriptive statistics in software. 8 - Practical usage of descriptive statistics. 9 - Estimation Theory and confidence intervals. 10 - Introduction to hypothesis testing. 11 - One sample t-test, independent two sample t-test and paired t-test. 12 - One-way ANOVA with fixed effects, comparison among means and multiple comparisons with an emphasis on conditions for this test. Nonparametric methods. 13 - Simple linear regression analysis. 14 - Summary of the course, questions.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
  • Preparation for classes - 85 hours per semester
  • Preparation for credit - 10 hours per semester
  • Preparation for exam - 46 hours per semester
  • Class attendance - 9 hours per semester
Learning outcomes
The aim of course is to introduce students to the basic statistical methods, acquire principles of inductive thinking and teach students to use statistical methods. In a lecture will be used data sets gained form other teachers and departments. Problems to solve will be primarily focused on specialization of students.
Students understand the basic principles of statistical methods and probability. Students are able to carry out hypothesis testing and regression analysis.
Prerequisites
The course has no prerequisities.

Assessment methods and criteria
Combined exam, Test

Attendance in seminars, working out seminar works and at least 50% of points from each credit test. Examination Requirements: Written and oral exam.
Recommended literature
  • Anděl, Jiří. Matematika náhody. Praha : Matfyzpress, 2003, 2003. ISBN 80-86732-07-X.
  • Anděl, Jiří. Statistické metody. Praha : Matfyzpress, 2003. ISBN 80-86732-08-8.
  • Čermáková, A., Rost, M. Výukové texty k základnímu kurzu Statistiky pro EF a ZF. 2004, dostupné online na.
  • Verzani J. Simple R. Using R for Introductory Statistics. New York : CSI Math, City University of New York, 2002. Dostupné online na.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Protection of Waters (2020) Category: Ecology and environmental protection 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Fishery (2020) Category: Agriculture and forestry 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Fishery (2019) Category: Agriculture and forestry 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Fishery (2018) Category: Agriculture and forestry 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Protection of Waters (2021) Category: Ecology and environmental protection 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Fisheries and Protection of Waters Study plan (Version): Fishery (2021) Category: Agriculture and forestry 1 Recommended year of study:1, Recommended semester: Summer