| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Mathematical Statistics |
| Course code | Ekon6034 |
| Credit points (ECTS) | 3 |
| Total Hours in Course | 81 |
| Number of hours for lectures | 12 |
| Number of hours for seminars and practical classes | 12 |
| Independent study hours | 57 |
| Date of course confirmation | 04/09/2019 |
| Responsible Unit | Institute of Computer Systems and Data Science |
| Course developers | |
| Dr. agr., prof. Līga Paura |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of this course is to provide students with parametric and non-parametric statistical methods and basic principles of statistical methods application to business economics science data analysis. Statistical methods will be illustrated with real data examples. The course subjects are: two independent samples tests, two related samples tests, contingency analysis, correlation and regression analysis. | |
| Learning outcomes and their assessment | |
| Knowledge depth knowledge and critical understanding about parametric and non-parametric data analysis methods; choose and apply methods according to research task (executed practical works, two test works, theory test); skills independently use statistical theory and choose parametric and nonparametric data analysis methods. Able to discuss about principles of choice the methods and their application and implementation of the specific problem at study (executed practical works, two test works, independent works); competences to realize data analysis in master work by using a data processing application software; to interpret the results and write conclusions relate to the sector of the business economy; to base decisions and to analyse them (executed theory test, independent works). | |
| Course Content(Calendar) | |
| 1.Introduction to statistics. Data classification. Data graphical presentation[L 1h,P 1h].
2.Descriptive statistics for quantitative variables[L 1h,P 1h]. 3.Hypothesis testing[L 1h]. 4.Parametric comparison of two independent samples – the F- test and t-test[L 1h,P 1h]. 5.Non-parametric comparison of two independent samples – the Mann-Whitney tests[L 1h,P 1h]. 6.Parametric method for comparing two paired samples – t-test[L 1h,P 1h]. 7.Non-parametric method for comparing two paired samples – the Wilcoxon tests[L 1h,P 1h]. 8.Contingency tables. Contingency analysis. Hypothesis testing. 1st test: descriptive statistics, two independent and paired samples[L 0.5h,P 1h]. 9.Free software and online tools for data analysis[L 0.5h,P 0.5h]. 10.Chi-Square Independence Test: 2x2 contingency tables[L 0.5h,P 0.5h]. 11.Fisher Exact test: 2x2 contingency tables[L 0.5h,P 0.5h]. 12.Chi-Square Independence Test: rxc contingency tables[L 1h,P 1h]. 13.Correlation analysis. Pearson correlation coefficient. Hypothesis testing[L 0.5h,P 0.5h]. 14.Correlation analysis. Spearmen’s rank correlation coefficient. Hypothesis testing[L 0.5h,P 1h]. 15.Simple linear regression. Hypothesis testing. 2nd test: contingency analysis, correlation and regression analysis[L 1h,P 1h]. |
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| Requirements for awarding credit points | |
| Two test works and theory test have been written. Independent works have been developed. Test works can be written only at specified time. Delayed works must be executed according to procedure described in LLU Study regulations. | |
| Description of the organization and tasks of students’ independent work | |
| 1st independent work: Nonparametric and parametric methods for two samples analysis. (Upload in e-system). 2nd independent work: Simple correlation and regression analysis, contingency analysis (Upload in e-system). | |
| Criteria for Evaluating Learning Outcomes | |
| Test evaluation depends on the semester cumulative assessment: two test works (1st test – 40%; 2nd test – 40%) and theory (20%). | |
| Compulsory reading | |
| 1. Arhipova I., Balina S. Statistika ekonomikā un biznesā. Risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 362 lpp. 2. Goša Z. Statistika: mācību grāmata. Rīga: LU, 2003. 334 lpp.
3. Paura L., Arhipova I. Neparametriskās metodes SPSS datorprogramma: mācību līdzeklis. Jelgava: LLKC, 2002. 148 lpp. 4. Krastiņš O., Ciemiņa I. Statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp. 5. 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 | |
| 1. Kabacoff R. I. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: Manning, 2015. 579 P. 2. Anderson D.R., Sweeney D.J., Williams T.A., Freeman J., Shoesmith E. Statistics for business and economics Fourth edition. Hampshire: Cengage Learning, 2017. 615 P. | |
| Notes | |
| Obligatory course for professional master’s study programmes “Business Management“ | |