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Biostatistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:4.00
Study Course Accepted:12.08.2022 11:18:18
| Study Course Information | |||||||||
| Course Code: | SL_014 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Rehabilitation | ||||||
| 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) | 8 | Lecture Length (academic hours) | 1 | Total Contact Hours of Lectures | 8 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 16 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school background in mathematics and informatics. Basic knowledge in research methods. | ||||||||
| Objective: | Aim of the course is to provide students with the knowledge and skills in general statistics and aplied mathematics in order to create comprehension about importance of on evidence-based medicine in the education of nutritionist. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction. Data collection, creation of database. Introduction to SPSS. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 2 | Presentation of data. Descriptive statistics. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 3 | Statistical hypothesis testing. Parametric methods. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 4 | Statistical hypothesis testing. Nonparametric methods. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 5 | Statistical hypothesis testing. Qualitative data. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 6 | Correlation theory elements. Regression analysis. | Lectures | 1.00 | computer room | |||||
| Classes | 1.00 | computer room | |||||||
| 7 | The concept of survival analysis. Concept of a factor, discriminant and cluster analysis. | Lectures | 2.00 | computer room | |||||
| 8 | Analysis of scientific publication. Analysis of research data project | Classes | 2.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the literature – prepare to lectures accordingly to plan; 2. Individual analysis of scientific publication. 3. Write a master's work data analyze plan project, | ||||||||
| Assessment Criteria: | Participation in practical lectures. For every missed lecture – summary has to be written using given literature (min. 1 A4 page). Student evaluation include: • Presentation of scientific research paper analysis (30%); • Presentation of statistical analysis plan for master work (20%); • Written exam (50%). | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After completion of this course, students will demonstrate basic knowledge that allow: * to recognise terminology used in statistics and basic methods used in different publications; * to know MS Excel and IBM SPSS offered data processing tools; * to know data processing method criterias; * to know how correctly interpret the most important statistical indicators. | ||||||||
| Skills: | After completion of this course, students will demonstrate skills: * to input and edit data in computer programs MS Excel and IBM SPSS; * to prepare data for statistical analysis correctly; * to choose appropriate data processing methods, incl., are 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 programmes with obtained results; * precisely describe obtained research results. | ||||||||
| Competencies: | After completion of this course, students will be able to argument 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 | Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp. (akceptējams izdevums) | ||||||||
| 2 | Barton, Belinda Peat, Jennifer. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2014. | ||||||||
| Additional Reading | |||||||||
| 1 | Field A. Discovering Statistics using IBM SPSS Statistics. 2018 | ||||||||
| 2 | Petrie A. & Sabin C. Medical Statistics at a Glance. 2020 | ||||||||
| Other Information Sources | |||||||||
| 1 | Latvijas Centrālā statistikas biroja dati adresē | ||||||||
| 2 | SPSS for Beginners. | ||||||||
