Course detail
Models of regression and time series
FAST-DA51Acad. year: 2009/2010
Regression function, linear regression model, method of least squares, confidence interval and testing hypotheses in the model.
Analysis of variance - one factor experiments, multiple-factor experiments.
Stochastic processes, distribution of stochastic processes, characteristics of stochastic process, point and interval estimate of these characteristics, stationary random processes, ergodic processes.
Decomposition of time series -moving averages, exponential smoothing.
Periodogram.
The Box-Jenkins approach (linear process, moving average process, autoregressive process, mixed autoregression-moving average process - identification of a model, estimation of parameters, verification of a model).
The use of statistical system STATGRAPHICS and EXCEL for time analysis.
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
2. Regular linear regression model.
3. Singular linear regression model
4. Analysis of variance - one factor experiments,
5. Analysis of variance - multiple - factor experiments,
6. Fundamental notions of stochastic process.
7. Stationary process. Ergodic process.
8. Decomposition of time series. Regression approach to trend.
9. Moving average.
10. Exponential smoothing.
11. Periodical model - periodogram.
12. Linear process. Moving average process - MA(q).
13. Autoregressive process - AR(p). Mixed autoregression - moving average process - ARMA(p,q).
Work placements
Aims
They should be familiar with the basic concepts of the theory of stochastic processes, know how to estimate the numeric characteristics of stochastic processes, estimate the trend component of a time series and set up foecasts. They should be able to judge the periodicity of a time series. Students should also get acquainted with the basic Box-Jenkins models.
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans