Course code Ekon4080

Credit points 3

Total Hours in Course81

Number of hours for lectures16

Number of hours for seminars and practical classes16

Independent study hours49

Date of course confirmation04.09.2019

Responsible UnitInstitute of Computer Systems and Data Science

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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