Skip to main content


Study Course Description

Course Description Statuss:Approved
Course Description Version:6.00
Study Course Accepted:28.07.2020
Study Course Information
Course Code:SL_004LQF level:Level 7
Credit Points:2.00ECTS:3.00
Branch of Science:Mathematics; Theory of Probability and Mathematical StatisticsTarget Audience:Dentistry
Study Course Supervisor
Course Supervisor:Māra Grēve
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)0Lecture Length (academic hours)0Total Contact Hours of Lectures0
Classes (count)11Class Length (academic hours)3Total Contact Hours of Classes33
Total Contact Hours33
Study course description
Preliminary Knowledge:
Secondary school background in mathematics and informatics.
To get basic knowledge and skills in data processing methods (descriptive statistics, methods of inferential statistics to estimate differences between groups and relationships between variables), to use in scientific work.
Topic Layout (Full-Time)
No.TopicType of ImplementationNumberVenue
1Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program. Descriptive statistics in MS Excel and IBM SPSS.Classes1.00computer room
2Descriptive statistics of the Normal distribution. Confidence intervals. Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculation.Classes1.00computer room
3Parametric statistics for quantitative data. Comparison of independent and depentend samples.Classes1.00computer room
4Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples.Classes1.00computer room
5Qualitative data processing. Independent and dependent samples.Classes1.00computer room
6Correlation analysis. Regression analysis (Linear regression).Classes1.00computer room
7Regression analysis (Binary logistic regression). ROC curvesClasses1.00computer room
8Survival analysis (Kaplan-Meier estimate and Cox proportional hazards regression model).Classes1.00computer room
9Summary and practical work with data using IBM SPSS. Analysis of scientific publications.Classes1.00computer room
10Independent work with data using IBM SPSS.Classes1.00computer room
11Students' presentations.Classes1.00computer room
Unaided Work:
1. Individual work with the literature – preparation to lectures according to the plan. 2. Individual analysis of scientific publication. 3. Individual work in pairs – every pair will get a research data file (or students can use their own) with previously defined research tasks. Students will analyse data to reach requirements of defined tasks using descriptive statistics and inferential statistics, show obtained results in a scientifically appropriate way and present them in the last lecture.
Assessment Criteria:
For successful integration of knowledge and to prepare for the final exam, the student performs the following activities (mandatory, not graded): 1. Participation in practical lectures. A practical assignment for each missed class. 2. Oral presentation of the analysis of a scientific publication. 3. Oral presentation of independent work. At the end of the course is a graded exam that consists of two parts, where: 50% – practical task with a database, 50% – multiple-choice test with 30 theoretical and practical questions in statistics.
Final Examination (Full-Time):Exam (Written)
Final Examination (Part-Time):
Learning Outcomes
Knowledge:After completion of this course, the student will demonstrate basic knowledge that allows to: * recognise terminology used in statistics and basic methods used in different publications; * know commonly used data processing tools in MS Excel and IBM SPSS; * know data processing criteria of various statistical methods; * interpret correctly the most important statistical indicators.
Skills:After completion of this course, the student will demonstrate skills to: * input and edit data in computer programs MS Excel and IBM SPSS; * prepare data for statistical analysis correctly; * choose appropriate data processing methods, incl., will be able to do statistical hypothesis testing; * statistically analyse research data using computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programmes with obtained results; * describe obtained research results correclty.
Competencies:After completion of this course, students will be able to argument and make decisions about statistical data processing methods, use them to achieve research aims, using computer programs MS Excel and IBM SPSS, practically use learned statistical basic methods to process research data.
Required Reading
1Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal, 2nd edition, John Wiley & Sons, 2014. ISBN-13: 978-1118589939
2Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, Sage Publications, 2013. ISBN-13: 978-1446249185
3Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2019. ISBN: 978-1-119-16781-5