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Mathematical Statistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:3.00
Study Course Accepted:15.12.2023 12:45:39
| Study Course Information | |||||||||
| Course Code: | SL_037 | LQF level: | Level 6 | ||||||
| Credit Points: | 3.00 | ECTS: | 4.50 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Sociology | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Silva Seņkāne | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 23 Kapselu street, 2nd floor, Riga, +371 67060897, statistika rsu[pnkts]lv, www.rsu.lv/statlab | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 12 | ||||
| Classes (count) | 6 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 12 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school knowledge in mathematics and informatics. | ||||||||
| Objective: | To get basic knowledge and skills in data processing methods (descriptive statistics, methods of inferential statistics to estimate differences between groups and relationships between variables), to use in bachelor thesis. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program. | Lectures | 1.00 | computer room | |||||
| 2 | Descriptive statistics in MS Excel and IBM SPSS. | Lectures | 0.50 | ||||||
| Classes | 0.50 | computer room | |||||||
| 3 | Descriptive statistics of the Normal distribution. Confidence intervals. | Lectures | 1.00 | computer room | |||||
| 4 | Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculation. | Lectures | 0.50 | computer room | |||||
| Classes | 0.50 | computer room | |||||||
| 5 | Parametric statistics for quantitative data. Comparison of independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 6 | Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 7 | Qualitative data processing. Independent and dependent samples. | Classes | 1.00 | computer room | |||||
| 8 | Correlation analysis. Regression analysis: Linear regression, Binary logistic regression. | Lectures | 0.50 | computer room | |||||
| Classes | 0.50 | computer room | |||||||
| 9 | Other multidimensional methods: factor analysis, cluster analysis. | Lectures | 0.50 | computer room | |||||
| Classes | 0.50 | computer room | |||||||
| 10 | Analysis of scientific publications. | Lectures | 1.00 | computer room | |||||
| 11 | Independent work with data using IBM SPSS. | Classes | 1.00 | computer room | |||||
| 12 | Student presentations. | Lectures | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the literature – preparation to lectures accordingly to plan. 2. Individual analysis of scientific publication. 3. Individual work in pairs – every pair will get a research data file (or students can use their own) with previously defined research tasks. Students will analyse data to reach requirements of defined tasks using descriptive statistics and inferential statistics, show obtained results in a scientifically appropriate way and present them in the last lecture. | ||||||||
| Assessment Criteria: | Participation in practical lectures. For every missed lecture – a practical task. On completion of this course – exam – 50% of the final grade; Oral presentation of scientific publication analysis – 25%; Oral presentation of independent work – 25%. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After completion of this course, the student will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know commonly used data processing tools in MS Excel and IBM SPSS; * know data processing criteria of various statistical methods; * interpret correctly the most important statistical indicators. | ||||||||
| Skills: | After completion of this course, the student will demonstrate skills to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., will be able to do statistical hypothesis testing; * statistically analyse research data using computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programs for obtained results; * describe obtained research results correctly. | ||||||||
| Competencies: | After completion of this course, students will be able to argue and make decisions about statistical data processing methods, use them to achieve research aims, using computer programs MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Field A. 2013. Discovering Statistics Using IBM SPSS Statistics, 4th ed, Sage Publications, ISBN-13: 978-1446249185 | ||||||||
| 2 | Arhipova, I. un Bāliņa, S. 2006. Statistika ekonomikā un biznesā.Risinājumi ar SPSS un MS Excel. Datorzinību centrs | ||||||||
| 3 | Acton C. and Miller R. 2009 SPSS for Social Scientists 2nd ed Palgrave Macmillan | ||||||||
