|Statuss(Aktīvs)||Izdruka||Arhīvs(0)||Studiju plāns Vecais plāns||Kursu katalogs||Vēsture|
|Course title||Statistical and Econometrical Methods|
|Total Hours in Course||160|
|Number of hours for lectures||16|
|Number of hours for seminars and practical classes||32|
|Independent study hours||112|
|Date of course confirmation||04/09/2019|
|Responsible Unit||Department of Control Systems|
|Dr. agr., prof. Līga Paura
Dr. sc. ing., prof. Irina Arhipova
|Mate5001, Mathematical Statistics
|The aim of this course is to provide students with parametric and non parametric statistical methods. The course goal is to check economy hypotheses, which contain the appropriated parameters, and convince, that the estimated parameters no conflicting with fundamental economy laws. The Econometrics course task is estimation of parameters of economy processes, utilizing economy theories or hypothesis formulation, data obtaining, economy theories or hypotheses mathematical and econometrics model specification.|
|Learning outcomes and their assessment|
|• Knowledge - is able to show the depth or extends knowledge and understanding about parametric and nonparametric data analysis methods (1st test, 1st home work, practical works); Known simple and multiple regression analysis. Define choose and apply methods according to research task; about the economic hypotheses, which include the relevant parameters, and which provides the basis for research, that the estimated parameters are not inconsistent with the fundamental economic laws (2nd test, 2nd home work, practical works); is able to apply knowledge of economic research related to the cross discipline fields (1st un 2nd home work, practical works).
• Skills – is able to independently use the theory, methods and problem-solving skills to carry out research activities in the economic evaluation of process parameters, using economic theory or hypothesis formulation (1st un 2nd test, 1st un 2nd home work, practical works). Is able to forcefully explain and discuss the data acquisition, economic theory and econometric model specification for the particular problem at study (home works). • Competence – is able to independently formulate and critically analyze the economic hypotheses in master thesis and to interpret the results.
|1 Introducton to R, R Studio: read and manipulating data, working with graphics, descriptive statistics.
2 The Normal distribution. Test for normality.
3 Parametric comparison of two independent samples – the F- test and t-test.
4 Non-parametric comparison of two independent samples – the Mann-Whitney tests.
5 Parametric method for comparing two paired samples – t-test.
6 Non-parametric method for comparing two paired samples – the Wilcoxon tests.
7 One-way Anova, two-way Anova.
8 Non-parametric method for comparing two or more independent samples: Kruskal-Wallis test.
9 Contingency tables. Chi-Square Independence Test.
10 1st test. Nonparametric and parametric methods for comparing two or more samples.
11 Simple linear regression. Hypothesis testing.
12 A measure of “goodness of fit”. The coefficient of determination. Correlation coefficient.
13 Functional forms of regression models. Models comparison.
14 Two factors linear regression model.
15 Multiple regression models comparison, comparing two regression models. 16 2nd test. Simple and multiple regression analysis. Hypothesis testing.
|Requirements for awarding credit points|
|Exam evaluation depends on the semester cumulative assessment: 1st test (40 points), 2nd test (40 points), theory (20 points). 10 points equal to 1 point of exam mark. Test works can be written only at specified time and once.|
|Description of the organization and tasks of students’ independent work|
|1st home work: Nonparametric and parametric methods for two and more samples analysis. (Upload in e-system). 2nd home work: Simple and multiple regression analysis. (Upload in e-system).|
|Criteria for Evaluating Learning Outcomes|
|Exam evaluation depends on the semester cumulative assessment (80%) and the theory (20%). The theory examination will be during the session. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (at least 4) hold the theory or examination. The exam includes practical part (80%) and theory (20%).|
|1. Arhipova I., Balina S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 359 lpp.
2. Krastiņš O. Ekonometrija. Rīga: LR Centrālā statistikas pārvalde, 2003. 207 lpp.
3. Paura L., Arhipova I. Neparametriskās metodes. SPSS datorprogramma: mācību līdzeklis. Jelgava: LLKC, 2002. 148 lpp.
4. Gujarati, Damodar N. Basic econometrics. 3rd ed. New York [etc.]: McGraw-Hill, Inc., 1995. 838 p. 5. Kabacoff R. I. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: Manning, 2015. 579 P.
|1. Goša Z. Statistika: mācību grāmata. Rīga: LU, Izglītības soļi, 2003. 334 lpp.
2. Gujarati D. N. Essentials of Econometrics. 2nd ed. Boston [etc.]: McGraw-Hill, Inc., 1999. 534 p.
3. Krastiņš O., Ciemiņa I. Statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp. 4. 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.
|Obligatory course for master study programme “Ekonomics”|