Veidlapa Nr. M-3 (8)
Study Course Description
Biostatistics
Course Code
SL_014
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Rehabilitation
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Vinita Cauce
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
23 Kapselu street, 2nd floor, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
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.
Preliminary Knowledge
Secondary school background in mathematics and informatics. Basic knowledge in research methods.
Learning Outcomes
Knowledge
1.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
1.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.
Competences
1.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.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual 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,
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
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%).
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Statistical hypothesis testing. Qualitative data.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Statistical hypothesis testing. Parametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical hypothesis testing. Qualitative data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Correlation theory elements. Regression analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction. Data collection, creation of database. Introduction to SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Presentation of data. Descriptive statistics.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Statistical hypothesis testing. Nonparametric methods.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Correlation theory elements. Regression analysis.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Introduction. Data collection, creation of database. Introduction to SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Analysis of scientific publication. Analysis of research data project
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
The concept of survival analysis. Concept of a factor, discriminant and cluster analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Analysis of scientific publication. Analysis of research data project
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Presentation of data. Descriptive statistics.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
The concept of survival analysis. Concept of a factor, discriminant and cluster analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical hypothesis testing. Nonparametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical hypothesis testing. Parametric methods.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
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.