Veidlapa Nr. M-3 (8)
Study Course Description
Biostatistics
Course Code
SL_015
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
Ināra Kantāne
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
Baložu iela 14, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
To get in-depth knowledge in data processing methods (descriptive statistics, inferential statistics to estimate characteristics of the entire population), that can be used in thesis work and in their speciality.
Preliminary Knowledge
Secondary school level knowledge in Mathematics and Informatics.
Learning Outcomes
Knowledge
1.On completion of the study course, students will demonstrate knowledge that allows to: * recognise terminology used in statistics and methods used in different publications; * know Excel and IBM SPSS Statistics offered data processing tools; * know data processing method criteria; * correctly interpret obtained research results.
Skills
1.On completion of this course, students will demonstrate skills to: * prepare data for statistical analysis correctly; * choose appropriate statistic data processing methods; * statistically analyse research data using computer programs Microsoft Excel and IBM SPSS Statistics; * create tables and graphs in Excel and IBM SPSS Statistics programs with obtained results; * precisely describe the obtained research results.
Competences
1.On 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 Excel and IBM SPSS Statistics, practically use acquired statistical methods to process research data.
Assessment
Individual work
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Title
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% from total grade
|
Grade
|
|---|---|---|
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1.
Individual work |
-
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-
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1. Individual work with the literature – prepare for lectures accordingly to the plan.
2. Individual analysis of a scientific publication.
3. Individual work – every student will receive a research data file (or the student can use their own) with previously defined research tasks. Student will process data to reach defined tasks using descriptive statistics, inferential statistics and/or analytical statistics methods. To report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture.
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.
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Examination
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
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Participation in practical lectures. For every missed lecture – a summary has to be written using given literature (min. 1 A4 page).
To complete this study course:
1. Oral presentation of scientific publication: 20% of the final grade.
2. Oral presentation of independent work: 30% of the final grade.
3. At the end of the study course, examination: test with theoretical and practical questions (30 questions): 50% of the final grade.
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Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
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3
|
Topics
|
Parametric statistics for quantitative data.
Comparison of independent and dependent samples.
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-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Nonparametric statistics for quantitative data.
Comparison of independent and dependent samples.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Qualitative data processing. Independent and dependent samples.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
3
|
Topics
|
Correlation analysis. Regression analysis (Linear regression).
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Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition, 2013.
2.
Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition, Wiley-Blackwell, 2019.
Additional Reading
1.
Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007, p 155.