STA_z Statistics

Institute of Technology and Business in České Budějovice
winter 2017
Extent and Intensity
2/4/0. 7 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Vladislav Biba, Ph.D. (seminar tutor)
Ing. Jiří Čejka, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Vladislav Biba, 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/D5: Sat 30. 9. 8:00–9:30 D315, 9:40–11:10 D315, 11:25–12:55 D315, Sun 1. 10. 14:50–16:20 D315, 16:30–18:00 D315, V. Biba
STA_z/D5a: Sat 4. 11. 13:05–14:35 D315, 14:50–16:20 D315, 16:30–18:00 D315, Sun 5. 11. 8:00–9:30 D315, 9:40–11:10 D315, Sun 10. 12. 8:00–9:30 D315, 9:40–11:10 D315, 11:25–12:55 D315, V. Biba
STA_z/P01: Wed 11:25–12:55 A2, V. Biba
STA_z/S01: Wed 13:05–14:35 D316, Thu 13:05–14:35 D216, V. Biba
Prerequisites (in Czech)
MAX_KOMBINOVANYCH(100) && MAX_PREZENCNICH(56) && MAT_2z Mathematics II
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 156 student(s).
Current registration and enrolment status: enrolled: 0/156, only registered: 0/156
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.
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
  • KERNS, G. J.: Introduction to Probability and Statistics Using R, 2010, ISBN 978-0-557-24979-4 (available online)
  • MONTGOMERY, D. C., RUNGER, G. C.: Applied Statistics and Probability for Engineers, John Wiley & Sons Inc, 2003, ISBN 978-0471204541
Forms of Teaching
Lecture
Exercise
Consultation
Teaching Methods
Frontal Teaching
Individual Work– Individual or Individualized Activity
Student Workload
ActivitiesNumber of Hours of Study Workload
Daily StudyCombined Study
Preparation for the Mid-term Test 26
Preparation for Seminars, Exercises, Tutorial2626
Preparation for the Final Test2652
Attendance on Lectures26 
Attendance on Seminars/Exercises/Tutorial/Excursion5226
Total:130130
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
Further Comments
The course is taught each semester.
The course is also listed under the following terms winter 2018, winter 2019, summer 2020, winter 2020, summer 2021, winter 2021, winter 2022, winter 2023, winter 2024.
  • Enrolment Statistics (winter 2017, recent)
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