| Statuss(Neaktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Statistical Data Analysis |
| Course code | Mate5012 |
| Credit points (ECTS) | 3 |
| Total Hours in Course | 81 |
| Number of hours for lectures | 16 |
| Number of hours for laboratory classes | 16 |
| Independent study hours | 49 |
| Date of course confirmation | 19/10/2011 |
| Responsible Unit | Institute of Computer Systems and Data Science |
| Course developers | |
| Dr. oec., doc. Līga Zvirgzdiņa |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of this course is to provide students with analysis of observed data, fitting the teoretical distributions, hipothesis testing, statistical measures estimation. Students acquire information about applied parametric and nonparametric methods, formal forecasting methods, multiple regression and correlation, and its application in the speciality research. | |
| Learning outcomes and their assessment | |
| As a result of the study course students acquire:in-depth knowledge and understanding of the latest research theoretical and methodological foundations, data collection and analysis methods, which provide the basis for creative thinking for research in the chosen specialty;skills to be able to use their own methods of mathematical statistics for statistically based opinion-making in research activities and master's work, an appropriate software application, student will be able direct its own skills development and specialization, with new approaches and innovation;competence - is able to independently formulate and critically analyze problems in the chosen field, choose the appropriate analytical methods, execute a professional evaluation and interpretation of results, integrate data analysis techniques in the creation of new knowledge, contribute to research and professional development. | |
| Compulsory reading | |
| 1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā. Rīga: Datorzinību Centrs, 2006. 362 lpp.
2. Grīnglazs L., Kopitovs J. Matemātiskā statistika. Ar datoru lietojuma paraugiem uzdevumu risināšanai. Rīga: RSEBA, 2003. 308 lpp. 3. Krastiņš O. Statistika. Rīga. LR Centrālā statistikas pārvalde, 2003. 267 lpp. 4. Paura L., Arhipova I. Neparametriskas metodes. SPSS datorprogramma. Jelgava: LLKC, 2002. 148 lpp. |
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| Further reading | |
| 1. Hair J.F., Anderson R.E., Tatham R.L., Black W.C. MultivariatedataanalysiswithReadings. 4th editon.USA: PrenticeHall, 1995.
2. Berenson M.L., Levine D.M. BasicBusinessStatistics. ConceptsandApplications. USA: PrenticeHall, 1999. 3. Mead R., Curnow R.N., Hasted AM. Statisticalmethodsinagricultureandexperimentalbiology. London: Chapman&Hall, 1993. |
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