| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Statistical Methods and Applications |
| Course code | VidZ5039 |
| Credit points (ECTS) | 3 |
| Total Hours in Course | 81 |
| Number of hours for lectures | 12 |
| Number of hours for seminars and practical classes | 12 |
| Number of hours for laboratory classes | 0 |
| Independent study hours | 57 |
| Date of course confirmation | 16/03/2022 |
| Responsible Unit | Instutute of Landscape Architecture and Environmental Engineering |
| Course developers | |
| Dr. sc.ing., asoc. prof. Laima Bērziņa |
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| Prior knowledge | |
| VidZ5038, Statistical Methods |
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| Course abstract | |
| The study course aims to master the methods of mathematical statistics thoroughly, emphasizing their practical significance in environmental, water and land engineering research. The study course continues to acquaint students with the methods of mathematical statistics. Students learn variance analysis, correlation, regression analysis, time series analysis and spatial data analysis, as well as method selection conditions and correct interpretation of results. | |
| Learning outcomes and their assessment | |
| After completing the study course, the student:
• knows and understands the application of analysis of variance, correlation, regression analysis, time series analysis, and spatial data analysis in accordance with the defined research objectives (1st and 2nd tests); • is able to practically apply the statistical methods in data analysis (practical assignments); • is capable of analyzing, systematizing, and critically evaluating data processing results within a specific field of research (home assignment). |
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| Course Content(Calendar) | |
| 1. Application of statistical methods in environmental, water and earth engineering [L 1h, P 1h].
2. Examples of application of variance analysis [L 1h, P 1h]. 3. Use of correlation analysis [L 1h, P 1h]. 4. Application of single-factor linear regression analysis [L 1h, P 1h]. Test 1: Use of variance and regression analysis. 5. Basics of nonlinear regression [L 2h, P 2h]. 6. Use of multifactor regression analysis [L 2h, P 2h]. 7. Methods of time series analysis [L 2h, P 2h]. 8. Application of statistical methods for spatial data analysis [L 2h, P 2h]. |
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| Requirements for awarding credit points | |
| Assessment. The cumulative grade consists of:
• 2 tests (including problem-solving tasks related to the master’s thesis topic); • Homework (a research assignment on the application of statistical methods in engineering studies). All assignments must be completed and submitted on time. Tests can only be taken at the specified time and only once. The assessment is considered passed if at least 70 points are obtained. |
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| Description of the organization and tasks of students’ independent work | |
| Preparation, visualization, and analysis of statistical data related to the master’s thesis topic. | |
| Criteria for Evaluating Learning Outcomes | |
| The study course evaluation depends on the cumulative evaluation of the study course tests and homework: Test 1: (40%). Test 2: (40%). Homework: research of the application of statistical methods, preparation of a report submitted in the e-learning environment, presentation of work results (20%). | |
| Compulsory reading | |
| Arhipova I., Balina S. Statistika ekonomikā un biznesā. Risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 362 lpp.
Smotrovs J. Varbūtību teorija un matemātiskā statistika II. Rīga: Zvaigzne ABC, 2007. 136 lpp. Brandt S. Data analysis: statistical and computational methods for scientists and engineers. 4th edition. Cham: Springer, 2014. 523 p. |
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| Further reading | |
| Mac Berthouex P., Brown L.C. Statistics for environmental engineers. Boca Raton etc.: Lewis Publishers, 2002. 489 p.
Shaw P. J. A. Multivariate Statistics for the Environmental Science. Wiley, 2009. 244 p. Helsel D.R., Hirsch R.M. Statistical Methods in Water Resources Techniques of Water Resources Investigations. U.S.: Geological Survey, 2002. 522 p. Gilbert R.O. Statistical Methods for Environmental Pollution Monitoring. John Wiley&Sons, 1987. 336 p. Gibbons R.D., Coleman D.E. Statistical methods for detection and quantification of environmental Contamination. John Wiley&Sons, 2001. 384 p. Chun Y., Griffith Daniel A. Spatial Statistics and Geostatistics. SAGE, 2013. 200 p Schabenberger, O., Gotway, C. Statistical Methods for Spatial Data Analysis. Chapman &Hall/CRC, 2005. 512 p. |
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| Periodicals and other sources | |
| 1. Official Statistics Portal [online] Available: https://stat.gov.lv/en
2. Eurostat [online] Available: https://ec.europa.eu/eurostat/web/main 3. FAOSTAT [online] Available: https://www.fao.org/faostat/en/#home 4. Science direct [online] Available: https://www.sciencedirect.com/ 5. Scopus [online] Available: https://www.scopus.com/home.uri |
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| Notes | |
| The course is compulsory for the academic master's study programme "Environmental, Water and Land Engineering" full-time studies | |