SubjectsSubjects(version: 853)
Course, academic year 2019/2020
  
Applied Statistics - S413004
Title: Applied Statistics
Guaranteed by: Department of Mathematics (413)
Actual: from 2017
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:1/2 C+Ex [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Level:  
For type:  
Guarantor: Zikmundová Markéta Mgr. Ph.D.
Kříž Pavel Mgr. Ing. Ph.D.
Examination dates   Schedule   
This subject contains the following additional online materials
Annotation
Last update: TAJ413 (17.12.2013)
The Elementary Course of Statistics is aimed at bachelor degrees students. Trainees will be able to solve elementary statistical methods and its connection with some probability concepts in necessary range providing to take up them in advanced statistical parts of other subjects.
Aim of the course
Last update: TAJ413 (17.12.2013)

Students are supposed to know:

Soft Competence:

1. To master fundamental statistical and probability concepts

2. Knowledge and acceptance of elementary statistical methods

Specific Competence:

3. To solve elementary statistical tasks with self-reliance

Literature
Last update: Kříž Pavel Mgr. Ing. Ph.D. (11.10.2019)

R: S.M. Ross: Introduction to Probability and Statistics for Engineers and Scientists (2014, Elsevier)

R: J.I. Barragués: Probability and Statistics – A didactic Introduction (2014, Taylor & Francis)

R: B. Bowerman, R.T. O'Counel: Applied Statistics (1997, IRWIN Inc Company)

Learning resources
Last update: Pavlík Jiří RNDr. CSc. (16.07.2013)

http://www.vscht.cz/mat/AS/TABLESANGL.pdf

Syllabus
Last update: Pavlíková Pavla RNDr. Ph.D. (13.04.2016)

1. Probability of random events, independence of random events.

2. Conditional probability, law of total probability, Bayes's theorem.

3. Random variable, distribution function, probability function, density.

4. Mean, variance, quantiles, median, critical values, independence and correlation of random variables.

5. Fundamental types of discrete and continuous distributions.

6. Random sample, sample statistics.

7. Point estimates, confidence intervals.

8. Testing of statistical hypotheses, type I and II errors. One-sample tests about mean and variance.

9. Two-sample tests about means and variances.

10. Independence testing.

11. Goodness-of-fit testing.

12. Contingency tables.

13. Fundamentals of regression analysis.

14. Summary, alternatively more specific statistical methods.

Registration requirements
Last update: Pavlík Jiří RNDr. CSc. (16.07.2013)

Mathematics I

 
VŠCHT Praha