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Mathematical Statistics I
Study Course Description
Course Description Statuss:Approved
Course Description Version:6.00
Study Course Accepted:12.08.2022 11:12:49
| Study Course Information | |||||||||
| Course Code: | SL_011 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Public Health | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Vinita Cauce | ||||||||
| 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) | 4 | Lecture Length (academic hours) | 1 | Total Contact Hours of Lectures | 4 | ||||
| Classes (count) | 5 | Class Length (academic hours) | 4 | Total Contact Hours of Classes | 20 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school knowledge in mathematics and informatics. | ||||||||
| Objective: | Enhance knowledge and practical skills about data analying methods, that are needed to master course Mathematical Statistics II, and to interpret statistical indicators used in public health. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to SPSS. Arithmetical functions. Data filters. Data transformations. Database creation and formatting. Data cleaning: missing values and outliers. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 2 | Descriptive statistic. Data types, measure. Frequency distribution. Central tendency measures. Measures of variability. Distribution indicators. Table and graph creating, correct formatting. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 3 | The concept of propability, theorethical distribuitions. Confidence intervals. Statistical hypothesis, types of statistical hypothesis. Parametric hypothesis methods (t-test, ANOVA). | Lectures | 1.00 | auditorium | |||||
| Classes | 1.00 | computer room | |||||||
| 4 | Nonparametric hypothesis testing methods (Mann-Whitney, Wilcoxon, Kruskall-Wallis, Friedman's test). | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 5 | Nonparametric hypothesis testing methods for categorical variables: 2 x 2, R x C crosstabs (χ2 chi square statistic, Fisher's Exact test). | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | Individual work with literature – preparation for the class, unknown terminology should be clarified, home tasks should be done. | ||||||||
| Assessment Criteria: | Active participation in practical lectures. Knowledge about statistical terminology and methods. Hometasks. Exam at the end of the course which consists of theoretical part (30-question test) - 50% and practical part -50%. For every missed lecture – a summary on the topic should be made (at least one page, size A4). | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | Upon successful acquisition of the course, the students will: * Recognise statistical terminology and basic methods used in scientific publications; * Know MS Excel, SPSS offered probabilities in data processing and visualising; * Know parametric and nonparametric methods criteria. | ||||||||
| Skills: | Upon successful acquisition of the course, the students will be able to: * Set up and edit database in MS Excel and SPSS; * Precisely prepare data for statistical analysis; * Create and edit tables, graphics; * Process data using computer programmes; * Choose correct data processing methods, that is to do statistical hypothesis; * Choose correct data analysis reporting methods to represent results. | ||||||||
| Competencies: | Upon successful acquisition of the course, the students will be able to interpet main statistical indicators in health science and practically use gained knowledge. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp. (akceptējams izdevums) | ||||||||
| 2 | Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 3 | Petrie A. & Sabin C. Medical Statistics at a Glance. 2020. | ||||||||
| 4 | Ārvalstu studentiem/For international students: | ||||||||
| 5 | Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 6 | Petrie A. & Sabin C. Medical Statistics at a Glance. 2020. | ||||||||
| Additional Reading | |||||||||
| 1 | Baltiņš M. Lietišķā epidemioloģija. Rīga: Zinātne, 2003. | ||||||||
