Veidlapa Nr. M-3 (8)
Study Course Description
Applied Biostatistics
Course Code
SL_033
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Dentistry; Medicine; Pharmacy; Public Health
LQF
All Levels
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Māris Munkevics
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
23 Kapselu street, 2nd floor, Riga, statistika@rsu.lv, +371 67060897
About Study Course
Objective
To introduce students with open access data analysis tool R and get acquainted with the possibilities in solving data analysis violations. The most commonly used data analysis methods have strong prerequisites that often are violated due to lack of expertise in addressing them. It is planned to introduce students with tools and methods to reduce data analysis violations.
Preliminary Knowledge
Basic knowledge in data analysis. Acquired, for example, in course SL_001 "Biostatistics" or its equivalents.
Learning Outcomes
Knowledge
1.Students will receive knowledge in programming in open access data analysis software and options in dealing with most common violations during data anlysis.
Skills
1.Students will be practically dealing with most common violations during data anlysis.
Competences
1.Frequently used data analysis methods have strong prerequisites that are often violated. The course participants will have the competence to address these violations analytically.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Every class will contain independent work – student individually prepares for them. Task solutions electronically submitable for evaluation.
In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Submitted tasks will be graded and cumulatively form 50% of the final grade. Remaining 50% will be formed by grade in the final test.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Introduction to R and RStudio.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
R graphics.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Quantitative data analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Correlations and simple regressions.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Multivariate analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Correlation and variance structures.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Mixed effects.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Meta-analysis.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Sokal, R.R. & Rohlf, F.J. 2009. Introduction to Biostatistics. 2nd edition.
2.
Dalgaard, P. 2008. Introductory Statistics with R. 2nd edition.
3.
Field, A., Miles, J., Field, Z. 2012. Discovering statistics using R.
Additional Reading
1.
Demidenko, E. 2013. Mixed models: theory and applications with R. 2nd edition
2.
Zuur, A., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M. 2009. Mixed Effects Models and Extensions in Ecology with R.
Other Information Sources
1.
Elferts D., Praktiskā biometrija, 2016, elektroniskā grāmata.