Veidlapa Nr. M-3 (8)
Study Course Description
Mathematical Statistics II
Course Code
SL_012
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
9.00
Target Audience
Public Health
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
To acquire in-depth knowledge, skills and abilities in specific mathematical statistical data processing methods for study purposes; for work in public health specialty; as well as promote the learning of statistical terminology and its practical application.
Preliminary Knowledge
Research methodology, basic topics in statistic, mathematics, knowledge in computer science.
Learning Outcomes
Knowledge
1.Upon successful acquisition of the course, the students will: Recognise statistical terminology and basic methods used in scientific publications; Know SPSS offered probabilities in data processing methods; Know different statistics methods role in scientific research work.
Skills
1.Upon successful acquisition of the course, the students will be able to: * Set up and edit database in SPSS; * Precisely prepare data for statistical analysis; * Create and edit tables, graphics; * Choose correct regression model; * Analyse time till event data; * Clarify tests Reliability and Validity; * Explain results; * Choose correct data analysis reporting methods to represent results.
Competences
1.Upon successful acquisition of the course, the students will interpret main statistical indicators in health science and practically use gained knowledge. To plan public health research work accordingly to data gathering and aggregation. Analyse processes and predict development.
Assessment
Individual work
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
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-
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Individual work with literature – unknown terminology must be studied, home tasks must be done.
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Examination
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Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
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|
Active participation in practical lectures;
Knowledge about statistical terminology and methods;
Examination of assigned homework.
Final exam of the study course, in which statistical terminology, as well as knowledge and practical application of methods are tested:
written part (tests) – 50%
practical task data processing – 50%,
For each missed lesson – a summary of the topic using the indicated literature (at least one A4 page).
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
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Modality
|
Location
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Contact hours
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|---|---|---|
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On site
|
Computer room
|
2
|
Topics
|
Incidence, prevalence, mortality. Direct standartization method.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Incidence, prevalence, mortality. Direct standartization method.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Factoral analysis.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Factoral analysis.
|
-
Lecture
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
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Topics
|
Discriminant analysis and cluster analysis.
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-
Class/Seminar
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
4
|
Topics
|
Discriminant analysis and cluster analysis.
|
-
Lecture
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Multivariate linear regression. Multicollinearity. General linear model (quantitative variables in regression analysis).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Multivariate linear regression. Multicollinearity. General linear model (quantitative variables in regression analysis).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Multivariate linear regression. Multicollinearity. General linear model (quantitative variables in regression analysis).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Multivariate linear regression. Multicollinearity. General linear model (quantitative variables in regression analysis).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Logistic regression. Model evaluation.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Logistic regression. Model evaluation.
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-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Logistic regression. Model evaluation.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Logistic regression. Model evaluation.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Multinomial regression, ordinal regression.
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-
Class/Seminar
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
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Multinomial regression, ordinal regression.
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
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Poisson regression.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Poisson regression.
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Survival analysis. Kaplan-Meier method. Survival analysis (Cox proportional hazards regression model).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Survival analysis. Kaplan-Meier method. Survival analysis (Cox proportional hazards regression model).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Survival analysis. Kaplan-Meier method. Survival analysis (Cox proportional hazards regression model).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Survival analysis. Kaplan-Meier method. Survival analysis (Cox proportional hazards regression model).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Statistical methods summary.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Statistical methods summary.
|
Total ECTS (Creditpoints):
9.00
Contact hours:
72 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp. (akceptējams izdevums)
2.
Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
3.
Petrie A. & Sabin C. Medical Statistics at a Glance. 2020.
4.
Ārvalstu studentiem/For international students:
5.
Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
6.
Petrie A. & Sabin C. Medical Statistics at a Glance. 2020.
Additional Reading
1.
Baltiņš M. Lietišķā epidemioloģija. Rīga: Zinātne, 2003.