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

prof. RNDr. Jaroslav Ramík, CSc.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.

Timetable

Fri 11:25–13:00 B207

• Timetable of Seminar Groups:

INMNASTZ/01: Fri 13:05–13:50 B207, 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

The aim of this course is to provide students with the necessary knowledge in a comprehensive way with statistical methods suitable to process multidimensional and time series data. Absolvent of this course will be able to use an appropriate mathematical apparatus to multidimensional and time series data processing and apply appropriate statistical software on necessary statistical calculations.

Syllabus

• Review of Basic Statistical Concepts
  Experiment, sample space, sample point, random variables. Sample and population characteristics. Graphical evaluation of data distribution. Estimation and hypothesis testing. Parametrical and non-parametrical hypothesis tests. Statistical software.
  Analysis of Variance (ANOVA)
  ANOVA and basic principles of experimental design. Analysis of variance with one factor and analysis of variance with two factors. Assumptions of ANOVA with two factors. Two-factor ANOVA without interaction and interaction. Kruskal-Wallis nonparametric ANOVA.
  Simple Linear Regression
  Definition of regression analysis - a simple, multiple, linear, nonlinear regression. Simple linear regression analysis - scatter diagram, regression, regression coefficients, adhesion, coefficient of determination, tests of hypotheses. Basic types of nonlinearities, Törnquist curves and their applications in economics.
  
  Multiple Linear Regression
  Basic terms in multiple linear regression analysis - criteria, predictors, regression hyperplane, the coefficient of determination. Using the regression analysis for nominal predictors and correlation coefficients. Application examples of the economic area (marketing research). Problems in regression analysis: multicollinearity, heteroscedasticity, autocorrelation
  Heteroscedasticity tests (Park test, Bartley test), autocorrelation test (sign test).
  Time Series Analysis
  Types of economic time series. Elemental characteristics of time series. Models of economic time series - decomposition, exponential smoothing, ARIMA. Analytical methods for the determination of trends of time series: regression analysis (least squares method, maximum likelihood method). Synthetic methods: moving averages, exponential smoothing. Analysis of seasonal and random components of time series. Exponential smoothing models of time series (simple, Holt, Winters model). Stochastic process and its stationarity. Fundamentals of ARIMA models: models AR, MA, I, ARIMA. Identification of ARIMA model using the autocorrelation function (ACF) and partial autocorrelation function (PACF). Calculation of ARIMA model, model verification, prediction of the ARIMA model.
  

Literature

required literature
• ASTERIOU, D., HALL, S. G. Applied Econometrics. 2nd ed. New York: Palgrave Macmillan, 2011. ISBN 0230271820. info
• GUJARATI, D. N. and PORTER, D. C. Essentials of Econometrics. 4th ed. New York: McGraw-Hill/Irwin, 2010. ISBN 0073375845. info

recommended literature
• KELLER, G. Statistics for Management and Economics. Southwestern / Cengage Learning, 2014. ISBN 978-1285425450. info
• HYNDMAN, R. J., ATHANASOPOULOS, G. Forecasting: Principles and Practice. www.otexts.org, 2013. ISBN 978-0987507105. URL info
• ANDERSON, D. R., SWEENEY, D. J., and WILLIAMS, T. A. Essentials of modern business statistics with Microsoft Office Excel. 5th Ed. Mason. Ohio: South-Western / Cengage Learning, 2011. ISBN 0840062389. info

Teaching methods

Lecture with presentation in Power-point

Assessment methods

Grade
Written exam

Language of instruction

English

Further comments (probably available only in Czech)

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

Teacher's information

attendance in seminars 50 %, ongoing tests, final written exam

Activity	Difficulty [h]
Ostatní studijní zátěž	61
Přednáška	26
Seminář	13
Zkouška	40
Summary	140

• Enrolment Statistics (Winter 2022, recent)
  
• Permalink: https://is.slu.cz/course/opf/zima2022/INMNASTZ