OPF:INMNASTZ Statistical Data Processing - Course Information

INMNASTZ Statistical Data Processing

Extent and Intensity

2/1/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. RNDr. David Bartl, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. David Bartl, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.

Timetable

Mon 11:25–13:00 A423

• Timetable of Seminar Groups:

INMNASTZ/01: Mon 13:05–13:50 A423, D. Bartl

Prerequisites (in Czech)

FAKULTA(OPF) && TYP_STUDIA(N) && FORMA(P)

Course Enrolment Limitations

The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 0/50, only registered: 0/50

fields of study / plans the course is directly associated with

there are 7 fields of study the course is directly associated with, display

Course objectives (in Czech)

Based on the basic knowledge of statistics, acquired e.g. in the Bachelor's degree course Statistics, to present and explain further notions and concepts of mathematical statistic, main findings of this theory, and also basic statistical and econometric methods. The contents of the course is presented with respect to economic applications. To acquire the necessary computing skills and to learn to solve statistical problems using Excel on the computer.

Learning outcomes (in Czech)

After completing the course, the student will be able to:
- process data statistically using the simple / multiple linear regression;
- accomplish simple non-linear regression;
- accomplish the statistical test whether a factor has an effect on the expected value of a variable using the one-way analysis of variance (ANOVA);
- accomplish the statistical test whether a factor has an effect on the expected value of a variable and the statistical test whether there are interactions between the factors using the two-way analysis of variance (ANOVA);
- analyse and predict the future values of a time series.

Syllabus (in Czech)

• 1. Introduction
  Elementary statistical concepts: random experiment, sample space, event space, random variable. Population and sample characteristics (mean value, variance). Point and interval estimates, hypothesis testing.
• 2. Analysis of variance (ANOVA)
  Basic principles of experimental design. Single factor or one-way ANOVA, assumptions, Bartlett's test for the equality of variances. Two-way ANOVA without and with replication. Three-way ANOVA – Latin squares. Kruskal-Wallis non-parametric ANOVA.
• 3. Linear regression and regression analysis
  Simple and multiple linear regression. The classical assumptions for the linear regression. Test of hypotheses for the parameters and confidence intervals. The coefficient of determination. Problems in regression analysis: multicollinearity and its causation; heteroscedasticity (Park test, Bartlett's test) and fixes for it; autocorrelation (sign test). Non-linear regression, basic types of non-linear regression, Törnqvist curves and their applications in economics.
• 4. Dummy variables
  ANOVA model with one qualitative variable and the corresponding regression model with dummy variables. A regression with a mixture of quantitative and qualitative variables (analysis of covariance, ANCOVA).
• 5. Time series analysis
  Types and elementary characteristics of economic time series. Decomposition of time series: trend, cyclical, seasonal, and random component. Analytical methods to determine the trend of the time series: least squares method, maximum likelihood method. Synthetic methods: moving averages, exponential smoothing. Analysis of the cyclical and seasonal component. Analysis of the random component (Durbin-Watson test for autocorrelation).
• 6. Box-Jenkins methodology
  Stochastic process. The autocorrelation function (ACF) and the partial autocorrelation function (PACF). Autoregressive (AR) and moving average (MA) process. Autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) process. Box-Jenkins model identification. Forecasting in linear regression, ARIMA and SARIMA models.

Literature

required literature
• GUJARATI, Damodar N. Essentials of Econometrics. Fifth Edition. Sage Publications, 2023. ISBN 978-1-0718-5039-8. info
• ASTERIOU, Dimitrios and Stephen G. HALL. Applied Econometrics. 4th edition. Bloomsbury Publishing, Bloomsbury Academic, 2021. ISBN 978-1-352-01202-6. info

recommended literature
• HYNDMAN, Rob J. and George ATHANASOPOULOS. Forecasting: Principles and Practice. OTexts, 2021. ISBN 978-0-9875071-3-6. URL info
• ANDERSON, David, Dennis J. SWEENEY, Thomas WILLIAMS, Jeffrey D. CAMM, James J. COCHRAN, Michael J. FRY and Jeffrey W. OHLMANN. Essentials of Modern Business Statistics with Microsoft® Excel®. 8th Edition. Cengage, 2020. ISBN 978-0-357-56952-8. info
• ANDERSON, David, Dennis J. SWEENEY, Thomas A. WILLIAMS, Jeffrey D. CAMM, James J. COCHRAN, James FREEMAN and Eddie SHOESMITH. Statistics for Business and Economics. 5th Edition. Cengage, 2020. ISBN 978-1-4737-6845-1. info
• KELLER, Gerald and Nicoleta GACIU. Statistics for Management and Economics. 2nd Edition. Cengage, 2019. ISBN 978-1-4737-6826-0. info

Teaching methods (in Czech)

lectures and seminars (exercises, problems, examples and case studies), individual working out a seminar paper (several problems from the areas taught)

Assessment methods (in Czech)

Requirements for the student: regular study, attendance at seminars min. 70 %, seminar paper, final test.
Assessment: attendance at seminars, seminar paper (30 % of assessment), written test (70 % of assessment).
Assessment methods: individual working out a seminar paper (solving several problems from the areas taught), final written test (several problems from the areas taught).

Language of instruction

English

Further Comments

Study Materials
The course can also be completed outside the examination period.

• Enrolment Statistics (recent)
  
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