Course code Ekon4080

Credit points 2

Total Hours in Course80

Number of hours for lectures16

Number of hours for seminars and practical classes16

Independent study hours48

Date of course confirmation04.09.2019

Responsible UnitDepartment of Control Systems

prof.
## Līga Paura

Dr. agr.

The course gives knowledge about data analysis methods, management and analysis in the different statistical software. The following topics are covered: descriptive statistics, graphical summarizing of data and qualitative data analysis methods.

• knowledge and critical understanding about quantitative data (1st test, home work, practical works) and qualitative data analysis methods (2nd test, practical works); how to apply data analyses methods in research projects (tests, home work);

• skills to discuss the choice of methods of data processing principles, their application and implementation, to interpret the results and draw conclusions (tests, home work, practical works);

• competence to use statistical software and realize data analysis outcomes in bachelor work (the home work is developed and presented).

1. Introducton to statistics. Data classification. Data graphical presentation. [L – 1h, P – 1h]

2. Frequency distributions for quantitative data. [L – 1h, P – 1h]

3. Statistical parameters for quantitative data. [L – 1h, P – 1h]

4. Correlation (Pearson). Hypothesis testing. [L – 1h, P – 1h]

5. Correlation (Spearman). Hypothesis testing. [L – 1h, P – 1h]

6. Simple linear regression. Hypothesis testing. [L – 2h, P – 1h]

7. A measure of “goodness of fit”. The coefficient of determination. Correlation coefficient. [L – 1h, P – 1h]

8. 1st test: Statistical parameters. Correlation and regression analysis. [P – 2h]

9. Contingency tables. Introduction to contingence analysis. [L – 2h, P – 1h]

10. Chi2 - test for independence: 2x2 contingency table. Hypothesis testing. [L – 1h, P – 1h]

11. Chi2 - test for for independence: rxc contingency table. Hypothesis testing. [L – 1h, P – 1h]

12. Chi2-test for equality of proportions. [L – 1h, P – 1h]

13. On-line data analytics tools. [L – 1h, P – 1h]

14 2nd test: Contingency tables. Chi2-tests. [P – 2h]

15 Introduction to time series analysis: multiplicative model. [L – 1h]

16 Introduction to time series analysis: additive model. [L – 1h]

Laboratory works have been developed. Successfully write two tests (80%). Independent work has been developed and defended (20%). Examination.

Independent work: select a time series data from the Statistics website. Use a multiplicative and additive model for time series analysis. Describe the obtained results. The volume of work at least 5 pages, is uploaded to e-system, and is defended.

Exam evaluation depends on the cumulative assessment of two tests (80%) and independent work (20%). Tests can be written only at specified time and once. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (have at least 4) have examination. The exam includes practical part (80%) and theory (20%). The exam will be during the period of individual studies and examinations.

1. Arhipova I., Bāliņa S. Statistika ekonomikā un biznesā. Rīga: Datorzinību centrs, 2006. 325 lpp.

2. Arhipova I., Bāliņa S. Statistika ekonomikā: risinājumi ar SPSS un Microsoft Excel. Rīga: Datorzinību centrs, 2003. 352 lpp.

3. Paura L., Arhipova I. Neparametriskās metodes. SPSS datorprogramma. Mācību līdzeklis. Jelgava: LLU, 2002. 148 lpp.

1. Brase C. H., Brase C.P. Understandable statistics: concepts and methods. Tenth edition. Boston, MA: Brooks/Cole, Cengage Learning, 2012. 719 p. 2. Sullivan M., Statistics: informed decisions using data. Upper Saddle River, N.J.: Prentice Hall is an imprint of Pearson, 2010. 788 p. 3. Berenson M.L., Levine D.M. Basic Business Statistics: Concepts and Applications. Upper Saddle, New Jersey: Prentice Hall, 1999, 1058 p. 4. SPSS for social scientists/ R. L. Miller ... [et al.]; consultant ed. Jo Campling, Basingstoke, Hants; New York: Palgrave Macmillan, 2002, 331 p.

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