Veidlapa Nr. M-3 (8)
Study Course Description
Mathematical Statistics II
Course Code
SL_009
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Public Health
LQF
Level 6
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
Enhance knowledge and practical skills about data analyse basic methods in SPSS, strengthen those skills with programmes like Epilinfo, etc.
Preliminary Knowledge
Course Mathematichal Statistics I should be taken before.
Learning Outcomes
Knowledge
1.Upon successful acquisition of the course, the students will know: * about statistical calculations in different programmes; * about correlation and regression analysis.
Skills
1.Upon successful acquisition of the course, the students will be able to: * do hypothesis testing with one or multiple samples; * interpret quantitative variable correlation; * calculate descriptive statistics estimators; make graphs un do hypothesis testing in MS Excel, SPSS, EpiInfo programmes, and use online statistical calculators; * interpret data processing results accordingly to their speciality.
Competences
1.As a result of successful training, students will be able to make practical use of computer programs and calculators in the study process and in the professional sphere for data processing.
Assessment
Individual work
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Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Individual work with literature, in EpiInfo program – prepare for lectures, unknown terminology should be found out, home tasks should be done.
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Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Active participation in practical lectures.
Individual work about advanced descriptive statistic and hypothesis testing, make calculations and interpet results. For every missed lecture – a summary should be prepared (at least one paper, size A4).
At the end of the study course, written examination: computerised testing (30 questions) on representative names and decision-making in data processing – 50%, practical resolution – 30%, independent work- 20%.
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Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Introduction. Measuring association in 2 x 2 contingency table. Measuring effect size in contigency table analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Estimating the incidence, mortality and prevelence or disease. Standartization.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Correlation. Lienear regression.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Program EpiInfo.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Program EpiInfo.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Other statistical programmes, calculators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Course summary. Individual work with data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Individual work presentation.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Teibe U. Bioloģiskā statistika, LU, 2007. SL_009
2.
Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
3.
Petrie A. & Sabin C. Medical Statistics at a Glance. 2020