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Supervisor
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_011LQF level:Level 7
Credit Points:2.00ECTS:3.00
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, www.rsu.lv/statlab
Study Course Planning
Full-Time - 1. Semester No.
Lectures (number)6Lecture Length (academic hours)2Total Contact Hours of Lectures12
Classes (number)6Class Length (academic hours)2Total Contact Hours of Classes12
Total Contact Hours24
Study course description
Preliminary Knowledge:
Secondary school background in mathematics and informatics.
Objective:
Enhance knowledge and practical skills about data analying methods, that are needed to master course Mathematical Statistics II, interpretation of statistical indicators used in public health.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Introduction to SPSS. Arithmetical functions. Data filters. Dtata transformations. Database creation and formatting. Data cleaning: missing values and outliers.Lectures1.00computer room
Classes1.00computer room
2Descriptive statistic. Data types, measure. Frequency distribution. Central tendency measures. Measures of variability. Distribution indicators. Table and graph creating, correct formatting.Lectures1.00computer room
Classes1.00computer room
3The concept of propability, theorethical distribuitions. Confidence intervals. Statistical hypothesis, types of statistical hypothesis. Parametric hypothesis methods (t-test, ANOVA).Lectures1.00auditorium
Classes1.00computer room
4Nonparametric hypothesis testing methods (Mann - Whitney, Wilcoxon, Kruskall - Wallis, Ffriedmans test).Lectures1.00computer room
Classes1.00computer room
5Nonparametric hypothesis testing methods: 2 x 2, R x C crosstabs (χ2 chi square statistic, Fishers Exact test)Lectures1.00computer room
Classes1.00computer room
6Correlation analysis. Regression analysis.Lectures1.00computer room
Classes1.00computer room
Assessment
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 MS Excel, SPSS offered probabilities in data processing and visualising Know parametric and nonparametric methods criterias
Skills:Upon successful acquisition of the course, the students will be able to: * Set up and edit database in MS Excel and SPSS; * Precisely prepare data for statistical analysis * create and edit tables, graphics * process data using computer programmes * choose correct data processing methods, that is do statistical hypothesis * 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
Bibliography
No.Reference
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. (2003) Lietišķā epidemioloģija. Rīga: Zinātne