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Biostatistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:6.00
Study Course Accepted:12.08.2022 10:54:54
| Study Course Information | |||||||||
| Course Code: | SL_006 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Pharmacy | ||||||
| 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) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 4 | Total Contact Hours of Classes | 32 | ||||
| Total Contact Hours | 32 | ||||||||
| Part-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 4 | Total Contact Hours of Classes | 32 | ||||
| Total Contact Hours | 32 | ||||||||
| 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, inferential statistics methods to estimate differences and analytical statistics), to use in scientific work. | ||||||||
| 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. | Classes | 1.00 | computer room | |||||
| 2 | Descriptive statistics for quantitative and qualitative data. Descriptive statistics of the Normal distribution. Creation of tables and diagrams, correct design. | Classes | 1.00 | computer room | |||||
| 3 | Hypothesis testing. Parametric and nonparametric tests for quantitative data. | Classes | 1.00 | computer room | |||||
| 4 | Hypothesis testing. Tests for qualitative data. | Classes | 1.00 | computer room | |||||
| 5 | Correlation and regression analysis. | Classes | 1.00 | computer room | |||||
| 6 | Regression analysis. ROC curves. | Classes | 0.50 | computer room | |||||
| 7 | Survival analysis. | Classes | 0.50 | computer room | |||||
| 8 | Sample size estimation (including clinical trials). Analysis of scientific publications. | Classes | 1.00 | computer room | |||||
| 9 | Students presentations. | Classes | 1.00 | computer room | |||||
| Topic Layout (Part-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. | Classes | 1.00 | computer room | |||||
| 2 | Descriptive statistics for quantitative and qualitative data. Descriptive statistics of the Normal distribution. Creation of tables and diagrams, correct design. | Classes | 1.00 | computer room | |||||
| 3 | Hypothesis testing. Parametric and nonparametric tests for quantitative data. | Classes | 1.00 | computer room | |||||
| 4 | Hypothesis testing. Tests for qualitative data. | Classes | 1.00 | computer room | |||||
| 5 | Correlation and regression analysis. | Classes | 1.00 | computer room | |||||
| 6 | Regression analysis. ROC curves. | Classes | 0.50 | computer room | |||||
| 7 | Survival analysis. | Classes | 0.50 | computer room | |||||
| 8 | Sample size estimation (including clinical trials). Analysis of scientific publications. | Classes | 1.00 | computer room | |||||
| 9 | Students presentations. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the literature – prepare to lectures accordingly to the plan. 2. Individual analysis of scientific publication. 3. Individual work – every student will receive a research data file (or student can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistic, inferential statistic and/or analytical statistics methods. As well as to report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture. | ||||||||
| Assessment Criteria: | Participation in practical classes. Examination of the practical application of the acquired statistical terms and methods. To get a successful grade: 1. Multichoice test about statistics – 50%; 2. Scientific publication analysis – 30%; 3. Individual work presentations – 20%. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | Exam (Written) | ||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After completion of this course, students will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know MS Excel and IBM SPSS offered data processing tools; * know data processing method criteria; * 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., 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 programmes with obtained results; * describe obtained research results correctly. | ||||||||
| 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: Latvijas Universitāte, 2007, 156 lpp. (akceptējams izdevums) | ||||||||
| 2 | A. Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 3 | Petrie A. & Sabin Caroline. Medical Statistics at a Glance. Willey Blackwell, 2020. | ||||||||
| 4 | Ārvalstu studentiem/For international students | ||||||||
| 5 | A. Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 6 | Petrie A. & Sabin Caroline. Medical Statistics at a Glance. Willey Blackwell, 2020. | ||||||||
| Additional Reading | |||||||||
| 1 | Altman D. Practical Statistics for Medical Research. Chapman & Hall, 1997, pp. 612. | ||||||||
| 2 | Medical Statistics : A Guide to SPSS, Data Analysis and Critical Appraisal (2) by Barton, BelindaPeat, Jennifer, 2014 | ||||||||
