Veidlapa Nr. M-3 (8)
Study Course Description
Nonparametric Statistical Methods
Course Code
SL_128
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Life Science
LQF
Level 7
Study Type And Form
Full-Time; Part-Time
Study Course Implementer
Course Supervisor
Maksims Zolovs
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
Baložu street 14, Riga, statistika@rsu.lv, +371 67060897
About Study Course
Objective
The objective of this course is to give students the in-depth knowledge of nonparametric methods in mathematical statistics. In biostatistical applications it is common that the sample sizes are small and the normality of data is questionable. Moreover, the classical t-test and ANOVA procedure require additionally homogeneity condition which is often violated. Nonparametric procedures often are used in those situations. Classical linear regression also requires normality assumption and is limited to describe only the linear dependence. Nonparametric smoothing techniques allow to estimate the regression function in a very general way. Resampling methods are popular especially for deriving confidence intervals. The software package Jamovi and R will be used for computation and case study applications.
Preliminary Knowledge
• Familiarity with probability theory and mathematical statistics.
• Basic knowledge in Jamovi and R is required.
Learning Outcomes
Knowledge
1.• understand knowledge of and are able to define concepts and procedures of nonparametric statistical procedures; • are acquainted with and are able to choose nonparametric statistical procedures in program Jamovi and R.
Skills
1.• perform nonparametric testing in R and interpret the results; • be able to perform data resampling methods.
Competences
1.• understand and support the importance of assumptions made in standard statistical methods; • be able to make justified decisions between parametric and nonparametric procedures for practical data analysis, demonstrate understanding and ethical responsibility for the potential impact of scientific results on the environment and society; • independently develop a correct statistical model, critically interpret and present the obtained results, if necessary, further analysis will be performed.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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1. Individual work with the course material in preparation to lectures according to plan.
2. Independently prepare homeworks by practicing the concepts studied in the course.
In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
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Examination
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Title
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% from total grade
|
Grade
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|---|---|---|
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1.
Examination |
-
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-
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Assessment on the 10-point scale according to the RSU Educational Order:
• 2 independent homeworks 50%.
• Attendance and active participation in practical classes – 25%.
• Final written exam – 25%.
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Basic concepts of nonparametric statistics: definitions and examples. Testing normality and other assumptions for classical parametric procedures. Transformations of data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Testing normality, homogeneity and other assumptions in classical statistical procedures using simulated and real datasets in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Classical nonparametric tests: basic concepts. Sing test and Wilcoxon test for the one-sample case.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparison of t-test, sign test and Wilcoxon test for the one-sample case in Jamovi and R. Confidence procedures and power simulations.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Wilcoxon rank-sum test and Wilcoxon signed-rank test in the two-sample case.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Wilcoxon rank-sum test and Wilcoxon signed-rank tests in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Nonparametric one and two-way ANOVA procedures. Friedman and Kruskal-Wallis tests. Post-hoc procedures.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Dataset analysis in program Jamovi and R using both parametric and nonparametric ANOVA procedures.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Nonparametric correlation tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Analysos of datasets - Comparison of groups and correlations in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Generalized Linear models regression tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice on regression models in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Generalized Linear mixed models regression tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice of creating regression models in Jamovi and R.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
28 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Basic concepts of nonparametric statistics: definitions and examples. Testing normality and other assumptions for classical parametric procedures. Transformations of data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Testing normality, homogeneity and other assumptions in classical statistical procedures using simulated and real datasets in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Classical nonparametric tests: basic concepts. Sing test and Wilcoxon test for the one-sample case.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Comparison of t-test, sign test and Wilcoxon test for the one-sample case in Jamovi and R. Confidence procedures and power simulations.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Wilcoxon rank-sum test and Wilcoxon signed-rank test in the two-sample case.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Wilcoxon rank-sum test and Wilcoxon signed-rank tests in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Nonparametric one and two-way ANOVA procedures. Friedman and Kruskal-Wallis tests. Post-hoc procedures.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Dataset analysis in program Jamovi and R using both parametric and nonparametric ANOVA procedures.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Nonparametric correlation tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Analysos of datasets - Comparison of groups and correlations in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Generalized Linear models regression tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice on regression models in Jamovi and R.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Generalized Linear mixed models regression tests.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Practice of creating regression models in Jamovi and R.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
21 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Lehmann, Erich Leo, and Howard J. D'Abrera. Nonparametrics: statistical methods based on ranks. Holden-Day. 1975.
2.
Wasserman, Larry. All of nonparametric statistics. Springer Science & Business Media. 2006.
Additional Reading
1.
Agresti, A., Franklin, C. A. Statistics: The Art and Science of Learning from Data. (3rd ed.). Pearson Education. 2013.
2.
Chan, Bertram KC. Biostatistics for epidemiology and public health using R. Springer Publishing Company. 2015.
3.
DasGupta, Anirban. Asymptotic theory of statistics and probability. Springer Science & Business Media. 2008.