Veidlapa Nr. M-3 (8)
Study Course Description
Mathematical Statistics I
Course Code
SL_011
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Public Health
LQF
Level 7
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 analying methods, that are needed to master course Mathematical Statistics II, and to interpret statistical indicators used in public health.
Preliminary Knowledge
Secondary school knowledge in mathematics and informatics.
Learning Outcomes
Knowledge
1.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
1.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.
Competences
1.Upon successful acquisition of the course, the students will be able to interpet main statistical indicators in health science and practically use gained knowledge.
Assessment
Individual work
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
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-
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|
Individual work with literature – preparation for the class, unknown terminology should be clarified, home tasks should be done.
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Examination
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Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Active participation in practical lectures.
Knowledge about statistical terminology and methods.
Hometasks.
Exam at the end of the course which consists of theoretical part (30-question test) - 50% and practical part -50%.
For every missed lecture – a summary on the topic should be made (at least one page, size A4).
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
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1
|
Topics
|
Introduction to SPSS. Arithmetical functions. Data filters. Data transformations. Database creation and formatting. Data cleaning: missing values and outliers.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Introduction to SPSS. Arithmetical functions. Data filters. Data transformations. Database creation and formatting. Data cleaning: missing values and outliers.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Descriptive statistic. Data types, measure. Frequency distribution. Central tendency measures. Measures of variability. Distribution indicators. Table and graph creating, correct formatting.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Descriptive statistic. Data types, measure. Frequency distribution. Central tendency measures. Measures of variability. Distribution indicators. Table and graph creating, correct formatting.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
The concept of propability, theorethical distribuitions. Confidence intervals. Statistical hypothesis, types of statistical hypothesis. Parametric hypothesis methods (t-test, ANOVA).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
The concept of propability, theorethical distribuitions. Confidence intervals. Statistical hypothesis, types of statistical hypothesis. Parametric hypothesis methods (t-test, ANOVA).
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Nonparametric hypothesis testing methods (Mann-Whitney, Wilcoxon, Kruskall-Wallis, Friedman's test).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Nonparametric hypothesis testing methods (Mann-Whitney, Wilcoxon, Kruskall-Wallis, Friedman's test).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Nonparametric hypothesis testing methods for categorical variables: 2 x 2, R x C crosstabs (χ2 chi square statistic, Fisher's Exact test).
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
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.