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Vinita Cauce

Study Course Description

Course Description Statuss:Approved
Course Description Version:1.00
Study Course Accepted:03.10.2018
Study Course Information
Course Code:SL_012LQF level:Level 7
Credit Points:6.00ECTS:9.00
Branch of Science:Mathematics; Theory of Probability and Mathematical StatisticsTarget Audience:Public Health
Study Course Supervisor
Course Supervisor:Vinita Cauce
Study Course Implementer
Structural Unit:Statistical Unit
The Head of Structural Unit:Andrejs Ivanovs
Contacts:Kapseļu iela 23, 2.stāvs, Rīga, +371 67060897, statistikaatrsu[pnkts]lv,
Study Course Planning
Full-Time - 1. Semester No.
Lectures (number)12Lecture Length (academic hours)2Total Contact Hours of Lectures24
Classes (number)12Class Length (academic hours)4Total Contact Hours of Classes48
Total Contact Hours72
Study course description
Preliminary Knowledge:
Research methodology, basic topics in statistic, mathematics, knowledge in computer science.
To get applied knowledge and skills in specific mathematical statistics data processing method for public health.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Indicators of association, confidence interval calculation. StratificationLectures1.00computer room
Classes1.00computer room
2Incidence, prevalence, mortality. Direct standartization methodLectures1.00computer room
Classes1.00computer room
3Multivariate linear regression. Multicollinearity.Lectures1.00computer room
Classes1.00computer room
4Overall linear model (quantitative variables in regression analysis).Lectures1.00computer room
Classes1.00computer room
5Logistic regression. Model evaluation.Lectures1.00computer room
Classes1.00computer room
6Multinomial regression, ordinal regression.Lectures1.00computer room
Classes1.00computer room
7Poisson regression and other regressions.Lectures1.00computer room
Classes1.00computer room
8Survival analysis. Kaplan-Meier method.Lectures1.00computer room
Classes1.00computer room
9Survival analysis (Cox proportional hazards regression model). Model evaluationLectures1.00computer room
Classes1.00computer room
10Factor analysis.Lectures1.00auditorium
11Discriminant analysis and cluster analysis.Lectures1.00computer room
Classes1.00computer room
12Statistical methods summary.Lectures1.00computer room
Classes1.00computer room
Unaided Work:
Individual work with literature - unknown terminology should be find out, home tasks should be done.
Assessment Criteria:
Active participation in practical lectures. Knowledge about statistical terminology and methods. Hometasks. Semester test, theortetical part and practial part. For every missed lecture - conspect should be made (at least one paper, size A4).
Final Examination (Full-Time):Exam (Written)
Final Examination (Part-Time):
Learning Outcomes
Knowledge: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: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.
Competencies:Upon successful acquisition of the course, the students will interpet main statistical indocators in health science and practically use gained knowledge . To plan pulbic health research work accordingly to data gathering and aggregation Analyse processes and predict development
1Teibe U. Bioloģiskā statistika. Rīga: LU 2007 - 156 lpp
2Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, ISBN-13: 978-1446249185, 2013.
3Petrie A. & Sabin C. Medical Statistics at a Glance, 3rd edition, 2009. ISBN: 978-1-405-18051-1
1Baltiņš M. Lietišķā epidemioloģija. Rīga: Zinātne, 2003.