Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 24/25.P.1.00

Description Period: 2024./25 academic year pavasara semestris

Study Course Accepted: -

Study Course Description

Mathematical Statistics II

Main Study Course Information

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

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.

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%.

Study Course Theme Plan

FULL-TIME
Part 1

  1. Introduction. Measuring association in 2 x 2 contingency table. Measuring effect size in contigency table analysis.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4

  1. Estimating the incidence, mortality and prevelence or disease. Standartization.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4

  1. Correlation. Lienear regression.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4

  1. Program EpiInfo.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4
2.
Class/Seminar
On site
Computer room
4

  1. Other statistical programmes, calculators.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4

  1. Course summary. Individual work with data.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4

  1. Individual work presentation.

EventType
Modality
Location
Contact hours
1.
Class/Seminar
On site
Computer room
4
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

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