Page 120 - handbook 20152016

Faculty of Science Handbook, Session 2015/2016

SIT2005 DATA ANALYSIS I elementary properties, integral and sequences, Fubini
theorem.
Statistical Analysis for mean, variance, count and
proportion: Hypothesis testing, confidence interval and Probability space and measure. Random variables.
tests of independence. Independence. Sums of random variables. Borel-Cantelli
Statistical analysis for regression and Correlation: Lemma. Convergence in distribution, in probability and
continuous response data, simple and multiple linear almost surely; weak and strong laws of large numbers,
model. central limit theorem. Law of Iterated Logarithm. Generating
functions: characteristic functions, moment generating
Statistical tests: Goodness of fit tests, ANOVA, functions.
Nonparametric test
Assessment:
Assessment: Continuous Assessment: 40%
Continuous Assessment: 50% Final Examination: 60%
Final Examination: 50%
Medium of Instruction:
Medium of Instruction: English
English
Humanity Skill:
Humanity Skill: CS3, CT3, TS2, LL2
CS3, CT3
References:
References: 1. Halsey Royden and Patrick Fitzpatrck, Real Analysis,
1. Tibco Spotfire S-Plus Guide to Statistics Volume 1, International Edition, 4/E, Pearson, 2010.
TIBCO Software Inc. 2. Robert G. Batle, The Elements of Integration and
2. Mann, Prem. S., (2003). Introductory Statistics, John Lebesgue Measure, John Wiley, 1995.
Wiley & Sons. 3. R.M. Dudley, Real Analysis and Probability,
3. Siegel, A.W., and Morgan, C.J., (1998). Statistics and Cambridge University Press, 2002.
Data Analysis, John Wiley & Sons. 4. Taylor, J.C. An Introduction to Measure and Probability
4. Evans, J.R. and Olson, D.L. (2002)Statistics, Data Theory.Springer, 1997.
Analysis and Decision Modeling and Student CD-
ROM (2nd Edition), Prentice Hall.
SIT3002 INTRODUCTION TO MULTIVARIATE
ANALYSIS
SIT2006 NON-PARAMETRIC STATISTICS
The use/application of Multivariate analysis. Managing and
Statistical hypotheses, binomial test, runs test, sign test, Handling Multivariate data. Matrix theory. Random vectors
contingency tables, median test, chi-square Goodness of and Matrices. Multivariate Normal Distribution. Wishart
Fit test. Some methods based on ranks. distribution and Hotellings distribution. Selected topics from
Graphical methods, Regression Analysis, Correlation,
Assessment: Principle Components, Factor Analysis, Discriminant
Continuous Assessment: 40% analysis and Clustering Methods.
Final Examination: 60%
Assessment:
Medium of Instruction: Continuous Assessment: 40%
English Final Examination: 60%

Humanity Skill: Medium of Instruction:
CS2, CT2, TS1, LL2, EM2 English

References: Humanity Skill:
1. W.W. Daniel, Applied Nonparametric Statistics, 2nd ed CS2, CT3, LL2, EM1
PWS-Kent,1990
2. J.D.Gibbons, Nonparametric methods for Quantitative References:
Analysis, American Science Press,Columbus, 1985 1. Johnson, K. A. & Wichern, D. W. (2002), Applied
3. W.J.Conover, Practical NonParametric Statistics, Multivariate Analysis, Prentice-Hall International,
th
Wiley,1980 (5 ed.).
4. M. Kraska-Miller Nonparametric statistics for social 2. C. Chatfield & A. J. Collins (1980), An Introduction to
and behavioral sciences, CRC Press Taylor & Francis Multivariate Analysis,Chapman & Hall.
Group, 2014 3. Anderson, T. A. (1984), An Introduction toMultivariate
nd
Statistical Analysis, Wiley (2 ed.).
SIT3003 COMPUTER INTENSIVE METHODS IN
SIT3001 INTRODUCTION TO PROBABILITY STATISTICS
THEORY
Computer generation of uniform and non-uniform random
An introduction to concepts and fundamentals of measure variables. Monte Carlo evaluation of integrals. Bootstrap
theory essential for a rigorous approach to the basics of and jackknife methods. Variance reduction techniques.
probability. Expectation-Maximization algorithm. Markov Chain Monte
Carlo methods.
Sequences and series of functions and sets, convergence,
limit infimum and limit supremum. Assessment:
Continuous Assessment: 40%
Rings and algebras of sets, construction of a measure. Final Examination: 60%
Measurable functions and their properties, Egorov's
theorem, convergence in measure. Lebesgue integral, its

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