VŠTE:STA_z Statistics - Course Information

STA_z Statistics

Extent and Intensity

2/4/0. 7 credit(s). Type of Completion: zk (examination).

Teacher(s)

Ing. Josef Šedivý (seminar tutor)
Ing. Martin Telecký, Ph.D. (seminar tutor)

Guaranteed by

Mgr. Tomáš Náhlík, Ph.D.
Department of Informatics and Natural Sciences – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Supplier department: Department of Informatics and Natural Sciences – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice

Timetable of Seminar Groups

STA_z/P01: Tue 13:05–14:35 A7, M. Telecký
STA_z/S01: Tue 14:50–16:20 A6, Tue 16:30–18:00 A6, M. Telecký
STA_z/T1: Sat 27. 3. 8:00–9:30 A7, 9:40–11:10 A7, 13:05–14:35 A7, 14:50–16:20 A7, Sun 18. 4. 8:00–9:30 A7, 9:40–11:10 A7, 11:25–12:55 A7, Sun 2. 5. 8:00–9:30 A7, 9:40–11:10 A7, 11:25–12:55 A7, Sun 16. 5. 8:00–9:30 A7, 9:40–11:10 A7, 11:25–12:55 A7, M. Telecký

Prerequisites (in Czech)

OBOR(CAP)

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 23 fields of study the course is directly associated with, display

Course objectives supported by learning outcomes

The aim of the course is to introduce the students with basic procedures in the field of statictical induction, methods of analysis of qualitative and quantitative features and with time series analysis elements. After the successful completion of the course, the student masters the basic procedures in the field of statistical induction, methods of analysis of qualitative and quantitative features and with time series analysis elements. The student is able to collect, process and present statistical data.

Learning outcomes

After the successful completion of the course, the student masters the basic procedures in the field of statistical induction, methods of analysis of qualitative and quantitative features and with time series analysis elements. The student is able to collect, process and present statistical data.

Syllabus

• 1. Methods of descriptive statistics (basic statistical concepts, statistical investigation phase, frequency distribution table, interval frequency distribution, two-dimensional frequency distribution tables, statistical graphs). 2. Basic statistical parameters and indices (arithmetic mean and median, variance and standard deviation, coefficient of variation, percentiles, the characteristics of the aggregated data, simple and composite indices, price indices). 3.Probability and probability summary and their numerical characteristics (random events and their probabilities, properties of probabilities, independent and conditioned random events and their probability and summary and compound probability, random variable and its probability distribution, mean value and dispersion). 4. Basic probability models (binomial distribution, Poisson and normal, their mean and variance, standard random variable, standard ordinary summary, tables, distribution and quantile functions and their tables). 5. Surveys, a division of selection characteristics and basics of statistical induction (basic and selection file, the importance and types of sample surveys, sample distributions, central limit theorem, characteristics of the sample distribution, selective distribution of diameter and the ratio, point and interval estimates of population mean and the ratio and variance, determination of sample size). 6. Testing of statistical hypotheses (null and alternative hypothesis, significance level test and its determination, identification of the refusal, the critical value test, the test criterion, the choice between two hypotheses, hypothesis about the test of mean, ratio and variance, p-value of the test). 7. Two-Sample tests (test of the hypothesis about compliance of two averages, variances of two shares, testing in small choice files). 8. Further tests and analysis of variance (compliance tests, testing of independence in contingency table, single-factor analysis of variance, the degree of power dependence). 9. Simple linear regression and correlation (causal and non-causal dependence, covariance and correlation, scatter plot, the method of least squares, regression line, correlation coefficient, other types of regression functions). 10. Statistical induction in the regression model (population and sample regression function, estimates of regression coefficients and their properties, the standard errors of regression parameters and their confidence intervals, tests in regression analysis). 11. Multivariate regression and prognostic application of regression (multivariate regression model, the partial regression coefficients and their interpretation, the coefficient of determination, significance tests of the model using regression to predict 12.-13. Introduction to the series (time series and its graph, continuous time series and interval time series, characteristic of time series, trend functions, balancing time series by moving averages).

Literature

required literature
• STUCHLÝ, Jaroslav. Statistické analýzy dat : vysokoškolská učebnice. 1. vyd. České Budějovice: Vysoká škola technická a ekonomická v Českých Budějovicích, 2015, 220 pp. ISBN 978-80-7468-087-8. info
• HINDLS,R. a kol.: Statistika pro ekonomy. Praha, Professional Publishing 2006, ISBN 80-86946-16-9

recommended literature
• STUCHLÝ, J.: Statistika. Studijní opora pro kombinované studium. VŠTE České Bu-dějovice, 2011 (v elektronické podobě)
• MAREK, L. a kol. Statistika v příkladech. Praha: Profesional Publishing 2013. ISBN 978-80-7431-218-5
• ARLTOVÁ, M., BÍLKOVÁ, D., ČENČÍK, P., JAROŠOVÁ, E., PECÁKOVÁ, I., POUROVÁ, Z.: Základy statistiky v příkladech. Tribun EU, 192 str., 2014. ISBN 978-80-263-0756-3

not specified
• MONTGOMERY, D. C., RUNGER, G. C.: Applied Statistics and Probability for Engineers, John Wiley & Sons Inc, 2003, ISBN 978-0471204541
• KERNS, G. J.: Introduction to Probability and Statistics Using R, 2010, ISBN 978-0-557-24979-4 (available online)

Forms of Teaching

Lecture
Exercise
Consultation

Teaching Methods

Frontal Teaching
Individual Work– Individual or Individualized Activity

Student Workload

Activities	Number of Hours of Study Workload
	Daily Study	Combined Study
Preparation for the Mid-term Test		26
Preparation for Seminars, Exercises, Tutorial	26	26
Preparation for the Final Test	26	52
Attendance on Lectures	26
Attendance on Seminars/Exercises/Tutorial/Excursion	52	26
Total:	130	130

Assessment Methods and Assesment Rate

Test – mid-term 30 %
Test – final 70 %

Exam conditions

Grading of the course: First Test + second test: maximum 30% (0-30 points), Final Test: maximum 70% (0-70 points). Successful graduates of the course have to get totally at least 70 points: A 100 – 90, B 89,99 – 84, C 83,99 – 77, D 76,99 – 73, E 72,99 – 70, FX 69,99 – 30, F 29,99 - 0.

Language of instruction

Czech

Teacher's information

Attendance in lessons is defined in a separate internal standard of ITB (Evidence of attendance of students at ITB). It is compulsory, except of the lectures, for full-time students to attend 70 % lesson of the subjet in a semester.

• Enrolment Statistics (summer 2021, recent)
  
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