Page 154 - buku panduan 20192020

Faculty of Science Handbook, Session 2019/2020

4. Evans, J.R., & Olson, D.L. (2002). Statistics, data 3. Rosenthal, J. S. (2006). A first look at rigorous
nd
analysis and decision modeling (2 ed.). Prentice probability theory (2 ed.). Singapore: World Scientific
nd
Hall. Publishing Company.
th
4. Wade, W. (2017). An introduction to analysis. (4 ed.).
England: Pearson.
SIT2006 NON-PARAMETRIC STATISTICS

Statistical hypotheses, binomial test, runs test, sign test, SIT3002 INTRODUCTION TO MULTIVARIATE
contingency tables, median test, chi-square Goodness of ANALYSIS
Fit test, median test, some methods based on ranks.
The use/application of multivariate analysis. Managing and
Assessment: handling multivariate data. Matrix theory. Random vectors
Continuous Assessment: 40% and matrices. Multivariate normal distribution. Wishart
Final Examination: 60% distribution and Hotellings distribution. Selected topics from
graphical methods, regression analysis, correlation,
Medium of Instruction: principal components, factor analysis, discriminant analysis
English and clustering methods.

Soft Skills: Assessment:
CS2, CTPS2, EM2 Continuous Assessment: 40%
Final Examination: 60%
References:
1. W. W. Daniel. (1990). Applied nonparametric statistics Medium of Instruction:
nd
(2 ed.). PWS-Kent. English
2. J. D.Gibbons. (1985). Nonparametric methods for
quantitative analysis. Columbus: American Science Soft Skills:
Press. CS2, CTPS3
3. W. J. Conover. (1980). Practical nonparametric
statistics. Wiley. References:
4. M. Kraska-Miller. (2014). Nonparametric statistics for 1. Johnson, K. A., & Wichern, D. W. (2002). Applied
social and behavioral sciences. CRC Press Taylor & multivariate analysis (5 ed.). Upper Saddle River, NJ:
th
Francis Group. Prentice-Hall International.
2. Chatfield, C., & Collins, A. J. (1980). An introduction to
multivariate analysis. Chapman & Hall.
SIT3001 INTRODUCTION TO PROBABILITY 3. Anderson, T. A. (1984), An introduction to multivariate
THEORY statistical analysis (2 ed.). New York: John Wiley.
nd

An introduction to concepts and fundamentals of measure
theory essential for a rigorous approach to the basics of SIT3003 COMPUTER INTENSIVE METHODS IN
probability. STATISTICS

Sequences and series of functions and sets, convergence, Computer generation of uniform and non-uniform random
limit infimum and limit supremum. variables. Monte Carlo evaluation of integrals. Bootstrap
and jackknife methods. Variance reduction techniques.
Rings and algebras of sets, construction of a measure. Expectation-Maximization algorithm. Markov Chain Monte
Measurable functions and their properties, Egorov's Carlo methods.
theorem, convergence in measure. Lebesgue integral, its
elementary properties, integral and sequences, Fubini Assessment:
theorem. Continuous Assessment: 40%
Final Examination: 60%
Probability space and measure. Random variables. Medium of Instruction:
Independence. Sums of random variables. Borel-Cantelli English
Lemma. Convergence in distribution, in probability and
almost surely; Weak and Strong Laws of Large Numbers, Soft Skills:
Central Limit Theorem. Law of Iterated Logarithm. CS3, CTPS3
Generating functions: characteristic functions, moment
generating functions. References:
rd
1. Ross, S. M. (2002). Simulation (3 ed.). Academic
Assessment: Press.
Continuous Assessment: 40% 2. Roberts, C.P., & Casella, G. (1999). Monte Carlo
Final Examination: 60% statistical methods. Springer.
3. Dagpunar, J. S. (2007). Simulation and Monte Carlo.
Medium of Instruction: Wiley.
English 4. Gentle, J. E., Härdle, W. K., & Mori, Y. (2012)
Handbook of computational statistics: Concepts and
Soft Skills: Methods. Springer.
CS3, CTPS3

References: SIT3004 APPLIED STOCHASTIC PROCESSES
1. Billingsley, P. (1995). Probability and measure (3
rd
ed.). New York: John Wiley. Time reversible Markov chains. Poisson processes.
2. Durrett, R. (2010). Probability: Theory and examples Continuous-time Markov chains and birth and death
th
(4 ed.). Cambridge: Cambridge University Press. processes. Brownian motion. Application to real-world
phenomena, such as in finance.

141