MZUZU UNIVERSITY
FACULTY OF SCIENCE, TECHNOLOGY AND INNOVATION
DEPARTMENT OF MATHEMATICS AND STATISTICS
1. PROGRAMME : BSc Maths & Statistics
(Honours)
2. SUBJECT : Statistics
3. LEVEL OF STUDY : 3
4. COURSE TITLE : Nonparametric Statistics
5. COURSE CODE : STAT 3602
6. DURATION : 16 Weeks
7. PRESENTED TO : Senate
8. PRESENTED BY : Dean, FoSTI
9. LECTURE HOURS PER WEEK : 3
10. TUTORIAL HOURS PER WEEK : 1
11. PRACTICAL HOURS PER WEEK : 1
12. STUDENT INDEPENDENT LEARNING HOURS PER WEEK : 12
13. TOTAL COURSE CREDITS : 12
14. PRE-REQUISITE COURSE CODE(S): STAT 3501 and STAT 3502
15. CO-REQUISITE COURSE CODE (S) : None
16. DELIVERY METHODS:
16.1 Mode of Delivery : Face-to-face
16.2 Teaching Methods : Lecturers, Tutorials and Practical
17. ASSESSMENT METHODS : At least 2 continuous assessment tests,
and one end of semester examination
18. ASSESSMENT WEIGHTING : 40% Continuous assessment
60% End of semester Examination
19. AIM(S) OF THE COURSE : To introduce students to basic
concepts of
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nonparametric statistical
methods as applied to real life
phenomena in the sciences and
humanities.
20. LEARNING OUTCOMES: A successful learner from this programme will be able to:
• State difference(s) between parametric and nonparametric tests;
• give advantages and disadvantages of nonparametric tests;
• decide when to use nonparametric methods when making inferences about
parameters;
• Carry out one-sample and two-sample (independent or dependent) tests;
• Carry out tests involving data from three or more dependent or independent
samples;
• Use Chi-square test in determining homogeneity or independence of categories;
• Test whether a given set of sample data can fit a particular description or model.
21. TOPICS OF THE COURSE :
21.1 Parametric and nonparametric methods
• Classes of nonparametric methods
• Advantages and disadvantages of nonparametric statistics
21.2 One-Sample Nonparametric Methods
• One-sample sign test and large sample approximation
• Wilcoxon signed-ranks test
• Confidence interval of median based on sign and Wilcoxon tests
• The binomial test and runs test for randomness
21.3 Tests for two Independent Samples
• The Mann-Whitney (Wilcoxon rank-sum) test
• Large-Sample Approximation
• Confidence interval for difference between two population medians
• Two-sample runs test for randomness
21.4 Procedures for data from Two Related Samples
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• The sign test for two related samples
• Confidence interval for the differences of medians of two populations based
on the sign test
• Wilcoxon matched-pairs signed-ranks test
• Large sample approximation
• Confidence interval for differences between pairs of measurements based on
the Wilcoxon matched-pair signed ranks test
• A test for two related samples when the data consist of frequencies (The
McNemar test)
21.5 Chi-Square Test of Homogeneity and Independence
• Chi-square test of homogeneity
• Chi-square test of independence
21.6 Procedures Using Data from Three or More Independent Samples
• The Kruskal-Wallis one-way analysis of variance by Ranks
• Chi-Square approximation
• The Jonckheere-Terpstra test for ordered alternatives
21.7 Procedures Using Data from Three or More Related Samples
• Data from a randomized complete block design
• Friedman two-way analysis of variance by ranks
• Page’s test for ordered alternatives
21.8 Goodness-of-Fit Tests
• The chi-square goodness-of-fit test
• Kolmogorov-Smirnov goodness-of-fit test
• The Kolmogorov–Smirnov goodness-of-fit test for a single sample
• The Kolmogorov–Smirnov two-sample test
21.9 Rank Correlation
• Spearman’s rank correlation coefficient
• Kendall’s rank correlation coefficient
22. PRESCRIBED TEXTS:
Deshpande, J.V., Naik-Nimbalkar, U. & Dewan, I. (2017) Nonparametric Statistics: Theory
and Methods, London: World Scientific Publishing Company
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Bluman, A. G. (2019) Elementary Statistics: A step by step approach, 8th Ed., London:
McGraw Hill.
Hastie, T., Tibshirani, R. & Friedman, J. (2017) The elements of statistical learning: Data
mining, inference and prediction, 2nd Ed., New York: Springer.
Ross, S.M. (2019) Introduction to probability models, 12th Ed., London: Elsevier
Corder, G.W. & Foreman, D.I. (2014) Nonparametric Statistics: A Step-by-Step Approach,
2nd Ed., New York: Wiley.
23. RECOMMENDED TEXTS:
Hollander, M., Wolfe, D.A. & Chicken, E. (2014) Nonparametric Statistical Methods, 3rd
Ed., New York: Wiley.
Gibbons, J. D. & Chakraborti, S. (2010) Nonparametric Statistical Inference, 5th Ed., New
York: Marcel Dekker.
Sprent, Peter and Smeeton, N. C. (2007) Applied Nonparametric Statistical Methods, 4th Ed.,
Boca Raton, Florida: CRC Press.
This course outline was approved by Senate on…………………………………………
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