Statistical Methods

Total Hours in Course81

Number of hours for lectures12

Number of hours for seminars and practical classes12

Number of hours for laboratory classes0

Independent study hours57

Date of course confirmation16.03.2022

Responsible UnitInstutute of Landscape Architecture and Environmental Engineering

Course developer

Datoru sistēmu un datu zinātnes institūts

Laima Bērziņa

Dr. sc.ing.

Course abstract

The study course aims to provide knowledge about statistical methods and their application in engineering research. Students gain an understanding of the importance of statistical methods in obtaining accurate judgments and logical conclusions. Students learn the classification of statistical methods, the conditions of methods choice, as well as the application for comparing the average values. In addition, the analysis of the association with the χ2 test is considered.

Learning outcomes and their assessment

After completing the study course, the student:
• knows and understands the classification of the main methods for data processing, and the fundamental principles for selecting statistical methods in accordance with the defined research objectives (1st and 2nd tests);
• is able to apply basic statistical methods in professional research (practical assignments);

• is capable of analyzing and systematizing data processing results and using them in research related to the specific characteristics of the field (home assignment).

Course Content(Calendar)

1. The role of statistical methods in engineering [L 1h, P 1h].
2. Acquisition of research data and processing planning [L 1h, P 1h].
3. Primary processing of statistical data, characterization of mean values and dispersion [L 1h, P 1h].
4. Types of statistical data visualization [L 2h, P 2h].
Test 1: description and visualization of statistical data.
5. Types of random size distribution and their determination [L 2h, P 2h].
6. Inferential statistics test statistical hypotheses [L 1h, P 1h].
7. Parametric and non-parametric statistical methods for characterization of the mean value [L 2h, P 2h].
8. Contingency analysis and χ2 as a test of statistical independence [L 2h, P 2h].
Test 2: Comparison of mean values and analysis of contingency.

Requirements for awarding credit points

Assessment. The cumulative grade consists of:
• 2 tests (maximum score: 40 and 40 points);
•Homework (practical data analysis task, maximum score: 20 points).

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.

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: data processing, preparation of a report submitted in an 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.
Krastiņš O., Ciemiņa I. Statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp.
Brandt S. Data analysis: statistical and computational methods for scientists and engineers. 4th edition. Cham: Springer, 2014. 523 p.

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

Notes

The course is compulsory for academic master's study programme "Environmental, Water and Land Engineering" full-time studies

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