Mathematical Statistics II

Study Course Description

Course Description Statuss:Approved

Course Description Version:4.00

Study Course Accepted:12.08.2022 11:15:17

Study Course Information
Course Code:		SL_012		LQF level:		Level 7
Credit Points:		6.00		ECTS:		9.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Public Health
Study Course Supervisor
Course Supervisor:		Vinita Cauce
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		23 Kapselu street, 2nd floor, Riga, +371 67060897, statistikarsu[pnkts]lv, www.rsu.lv/statlab
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		12	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		24
Classes (count)		12	Class Length (academic hours)		4	Total Contact Hours of Classes		48
Total Contact Hours								72
Study course description
Preliminary Knowledge:
Research methodology, basic topics in statistic, mathematics, knowledge in computer science.
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.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Incidence, prevalence, mortality. Direct standartization method.			Lectures		1.00	computer room
				Classes		1.00	computer room
2	Factoral analysis.			Lectures		1.00	computer room
				Classes		1.00	computer room
3	Discriminant analysis and cluster analysis.			Lectures		1.00	auditorium
				Classes		1.00	auditorium
4	Multivariate linear regression. Multicollinearity. General linear model (quantitative variables in regression analysis).			Lectures		2.00	computer room
				Classes		2.00	computer room
5	Logistic regression. Model evaluation.			Lectures		2.00	computer room
				Classes		2.00	computer room
6	Multinomial regression, ordinal regression.			Lectures		1.00	computer room
				Classes		1.00	computer room
7	Poisson regression.			Lectures		1.00	computer room
				Classes		1.00	computer room
8	Survival analysis. Kaplan-Meier method. Survival analysis (Cox proportional hazards regression model).			Lectures		2.00	computer room
				Classes		2.00	computer room
9	Statistical methods summary.			Lectures		1.00	computer room
				Classes		1.00	computer room
Assessment
Unaided Work:
Individual work with literature – unknown terminology must be studied, home tasks must be done.
Assessment Criteria:
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).
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 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.
Bibliography
No.	Reference
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.