LV EN RU DE FR

Mathematical Statistics

Course developer

Course abstract

Students learn the classification of mathematical statistical methods, their selection criteria, as well as application for comparison of mean values and analysis of relationship.
The main emphasis is placed on practical research guidelines and professional evaluation and interpretation of calculation results. The aim of the course is to provide knowledge about the use of mathematical statistics in engineering and understanding of the importance of statistical data analysis in research, as well as the application of statistical methods for obtaining accurate judgments and logical conclusions.

Learning outcomes and their assessment

Knowledge – ability to demonstrate in-depth knowledge of the classification of mathematical statistical methods and their application to interpret results in accordance with research tasks (practical work).
Skills – successfully apply descriptive statistics, as well as hypothesis testing methods, to compare mean values and analyze the relationship between variables; ability to explain the principles of used methods and obtained results (practical work, three tests).
Competence – independent planning of the application of mathematical statistics methods; analyzing and systematizing results of data processing results to use them for evaluation of cadaster and real estate (independent work).

Course Content(Calendar)

Full-time studies:
1. The role of statistics in engineering. Application of mathematical methods in land surveying (2h).
2. Random variables and probability distributions (2h).
3. Random sampling and data description (2h).
4. Types of descriptive statistics (2h).
5. Evaluation of population parameters. Statistical hypotheses. Classification of statistical tests (2h).
6. Test Nr.1 (2h).
7. Normal distribution law (2H).
8. t-test for dependent samples (2h).
9. t-test for independent samples and Fisher's F test (2h).
10. Test Nr.2 (2h).
11. Analysis of correlation (2h).
12. Simple linear regression analysis (2h).
13. Basics of nonlinear regression (2h).
14. χ2 as a statistical independence test (2h).
15. Non-parametric statistical methods (2h).
16. Test Nr.3 (2h).
Part-time studies:
All topics specified for full-time studies are implemented, but the number of contact hours is 1/2 of the specified number of hours

Requirements for awarding credit points

3 tests during the course;
submission of independent work results.

Description of the organization and tasks of students’ independent work

During the semester the student has to perform independent work. The work must be written and submitted electronically on the e-learning site.

Criteria for Evaluating Learning Outcomes

70 points of total cumulative course grading must be reiceived:
independent work – maximum 10 points;
tests – maximum 90 points.

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā. Rīga: Datorzinību Centrs, 2006. 362 lpp.
2. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību Centrs, 2003. 349 lpp.
3. Smotrovs J. Varbūtību teorija un matemātiskā statistika. II Rīga: Zvaigzne ABC, 2007.
4. Grīnglazs L., Kopitovs J. Matemātiskā statistika: ar datoru lietojuma paraugiem uzdevumu risināšanai. Rīga: Rīgas Starptautiskās ekonomikas un biznesa administrācijas augstskola, 2003.

Further reading

1. Krastiņš O. Ciemiņa I. Statistika. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp.
2. Krastiņš O. Statistika un ekonometrija. Rīga: LR Centrālā statistikas pārvalde, 1998. 435 lpp.

Periodicals and other sources

Centrālās statistikas pārvaldes mājas lapa [tiešsaiste]. Pieejams: https://www.csb.gov.lv

Notes

Professional bachelor study program "Land Management and Surveying" full-time studies and part-time studies.