INMNASDP Statistical Data Processing

School of Business Administration in Karvina
Winter 2022
Extent and Intensity
2/1/0. 6 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.
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.
Syllabus
  • 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, Törnquist 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.
Language of instruction
English
Further Comments
The course can also be completed outside the examination period.
The course is also listed under the following terms Winter 2023, Winter 2024.
  • Enrolment Statistics (Winter 2022, recent)
  • Permalink: https://is.slu.cz/course/opf/zima2022/INMNASDP