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Mathematical Statistics I

Study Course Description

Course Description Statuss:Approved
Course Description Version:3.00
Study Course Accepted:04.08.2020
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,,
Study Course Planning
Full-Time - 1. Semester No.
Lectures (count)6Lecture Length (academic hours)1Total Contact Hours of Lectures6
Classes (count)6Class Length (academic hours)3Total Contact Hours of Classes18
Total Contact Hours24
Study course description
Preliminary Knowledge:
Secondary school knowledge in mathematics and informatics.
Enhance knowledge and practical skills about data analying methods, that are needed to master course Mathematical Statistics II, and to interpret statistical indicators used in public health.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Introduction to SPSS. Arithmetical functions. Data filters. Data 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, Friedman's test).Lectures1.00computer room
Classes1.00computer room
5Nonparametric hypothesis testing methods for categorical variables: 2 x 2, R x C crosstabs (χ2 chi square statistic, Fisher's Exact test).Lectures1.00computer room
Classes1.00computer room
6Correlation analysis. Regression analysis.Lectures1.00computer room
Classes1.00computer room
Unaided Work:
Individual work with literature – preparation for the class, unknown terminology should be clarified, home tasks should be done.
Assessment Criteria:
Active participation in practical lectures. Knowledge about statistical terminology and methods. Hometasks. Exam at the end of the course which consists of theoretical part and practical part. For every missed lecture – a summary on the topic should be made (at least one page, 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 criteria.
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 to do statistical hypothesis; * Choose correct data analysis reporting methods to represent results.
Competencies:Upon successful acquisition of the course, the students will be able to interpet main statistical indicators in health science and practically use gained knowledge.
Required Reading
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
Additional Reading
1Baltiņš M. (2003) Lietišķā epidemioloģija. Rīga: Zinātne