SubjectsSubjects(version: 893)
Course, academic year 2021/2022
  
Statistics 1 - AB501009
Title: Statistics 1
Guaranteed by: Department of Economics and Management (837)
Actual: from 2020
Semester: winter
Points: winter s.:6
E-Credits: winter s.:6
Examination process: winter s.:
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Level:  
For type: Bachelor's
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Koťátková Stránská Pavla Ing. Ph.D.
Is interchangeable with: B501009
Examination dates   Schedule   
This subject contains the following additional online materials
Literature -
Last update: Krajčová Jana Mgr. Ph.D., M.A. (15.09.2020)

R: LIND, D., MARCHAL, W., WATHEN, S. (2015), Statistical Techniques in Business and Economics, (16th Edition). McGraw-Hill Education.

R: TRIOLA, M., F. (2015), Essentials of Statistics (5th Edition), Pearson Education.

R: LEVINE, SZABAT, STEPHAN (2016), Business Statistics: A First Course. New York: Pearson Global Edition.

R: ZÁŠKODNÝ, Přemysl (2012), The Principles of Probability and Statistics (Data Mining Approach). Praha: Curriculum.

Requirements to the exam - Czech
Last update: Fialová Jana (14.01.2020)

Zápočet: aktivní účast na cvičeních, zpracování korespondenčních úkolů, závěrečné zápočtové písemné práce

Zkouška: písemná - část teoretická a část praktická

Syllabus
Last update: Krajčová Jana Mgr. Ph.D., M.A. (08.02.2021)

1. Introduction to statistics – types of data, surveys, data processing, representation of the data, graphs and tables

2. Descriptive statistics – central tendency and variability for the population and for the sample,

3. Descriptive statistics – measures of skewness and kurtosis, variance decomposition

4. One-dimensional discrete random variable – probability mass function, cumulative density function, selected discrete distributions

5. One-dimensional continuous random variable – probability density function, cumulative density function, selected discrete distributions

6. Random vectors – probability density function, cumulative density function, joint, marginal and conditional probability, independence, basic characteristics

7. Basics of probability theory - random variable, frequency, probability distribution and its main characteristics, probability function, density, distribution function and their basic properties

8. Statistical inference – population and a sample, point and interval estimates of mean and variance

9. Testing statistical hypotheses – null and alternative hypothesis, level of significance, critical values, rejection interval, type I and type II errors, p-value, one-sided and two-sided alternative hypothesis

10. Basic parametric tests - tests about mean (one-sample), tests about variance (one -sample)

11. Basic parametric tests - tests about mean (two-sample dependent and independent), tests about variance (two-sample)

12. Basic non-parametric tests - Mann-Whitney, Wilcoxon rank-sum

13. Basic non-parametric tests - sign test, Kolmogorov-Smirnov test etc.

14. Final recap

Course completion requirements -
Last update: Botek Marek Mgr. Ing. Ph.D. (17.01.2020)

Credit can be awarded to student based on his participation in practical exercises and submitted homework during the semester. Alternatively student can pass a test at the end of the semester, with minimum required score of 60%. The minimum required attendance rate to seminars is 75%. The details will be agreed upon with the seminar instructor at the beginning of the semester.

A credit is required to allow a student to take the final exam. The final exam will cover both, the theory and the practical exercises. The exam will be in written form but can be complemented by oral examination.

 
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