Nonparametric Statistical Methods

Study Course Description

Course Description Statuss:Approved

Course Description Version:3.00

Study Course Accepted:14.03.2024 11:48:42

Study Course Information
Course Code:		SL_128		LQF level:		Level 7
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Life Science
Study Course Supervisor
Course Supervisor:		Maksims Zolovs
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		Baložu street 14, Riga, statistikarsu[pnkts]lv, +371 67060897
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		7	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		14
Classes (count)		7	Class Length (academic hours)		2	Total Contact Hours of Classes		14
Total Contact Hours								28
Part-Time - Semester No.1
Lectures (count)		7	Lecture Length (academic hours)		1	Total Contact Hours of Lectures		7
Classes (count)		7	Class Length (academic hours)		2	Total Contact Hours of Classes		14
Total Contact Hours								21
Study course description
Preliminary Knowledge:
• Familiarity with probability theory and mathematical statistics. • Basic knowledge in Jamovi and R is required.
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.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Basic concepts of nonparametric statistics: definitions and examples. Testing normality and other assumptions for classical parametric procedures. Transformations of data.			Lectures		1.00	auditorium
2	Testing normality, homogeneity and other assumptions in classical statistical procedures using simulated and real datasets in Jamovi and R.			Classes		1.00	computer room
3	Classical nonparametric tests: basic concepts. Sing test and Wilcoxon test for the one-sample case.			Lectures		1.00	auditorium
4	Comparison of t-test, sign test and Wilcoxon test for the one-sample case in Jamovi and R. Confidence procedures and power simulations.			Classes		1.00	computer room
5	Wilcoxon rank-sum test and Wilcoxon signed-rank test in the two-sample case.			Lectures		1.00	auditorium
6	Wilcoxon rank-sum test and Wilcoxon signed-rank tests in Jamovi and R.			Classes		1.00	computer room
7	Nonparametric one and two-way ANOVA procedures. Friedman and Kruskal-Wallis tests. Post-hoc procedures.			Lectures		1.00	auditorium
8	Dataset analysis in program Jamovi and R using both parametric and nonparametric ANOVA procedures.			Classes		1.00	computer room
9	Nonparametric correlation tests.			Lectures		1.00	auditorium
10	Analysos of datasets - Comparison of groups and correlations in Jamovi and R.			Classes		1.00	computer room
11	Generalized Linear models regression tests.			Lectures		1.00	auditorium
12	Practice on regression models in Jamovi and R.			Classes		1.00	computer room
13	Generalized Linear mixed models regression tests.			Lectures		1.00	auditorium
14	Practice of creating regression models in Jamovi and R.			Classes		1.00	computer room
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Basic concepts of nonparametric statistics: definitions and examples. Testing normality and other assumptions for classical parametric procedures. Transformations of data.			Lectures		1.00	auditorium
2	Testing normality, homogeneity and other assumptions in classical statistical procedures using simulated and real datasets in Jamovi and R.			Classes		1.00	computer room
3	Classical nonparametric tests: basic concepts. Sing test and Wilcoxon test for the one-sample case.			Lectures		1.00	auditorium
4	Comparison of t-test, sign test and Wilcoxon test for the one-sample case in Jamovi and R. Confidence procedures and power simulations.			Classes		1.00	computer room
5	Wilcoxon rank-sum test and Wilcoxon signed-rank test in the two-sample case.			Lectures		1.00	auditorium
6	Wilcoxon rank-sum test and Wilcoxon signed-rank tests in Jamovi and R.			Classes		1.00	computer room
7	Nonparametric one and two-way ANOVA procedures. Friedman and Kruskal-Wallis tests. Post-hoc procedures.			Lectures		1.00	auditorium
8	Dataset analysis in program Jamovi and R using both parametric and nonparametric ANOVA procedures.			Classes		1.00	computer room
9	Nonparametric correlation tests.			Lectures		1.00	auditorium
10	Analysos of datasets - Comparison of groups and correlations in Jamovi and R.			Classes		1.00	computer room
11	Generalized Linear models regression tests.			Lectures		1.00	auditorium
12	Practice on regression models in Jamovi and R.			Classes		1.00	computer room
13	Generalized Linear mixed models regression tests.			Lectures		1.00	auditorium
14	Practice of creating regression models in Jamovi and R.			Classes		1.00	computer room
Assessment
Unaided Work:
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.
Assessment Criteria:
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%.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		• 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:		• perform nonparametric testing in R and interpret the results; • be able to perform data resampling methods.
Competencies:		• 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.
Bibliography
No.	Reference
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.