Page 90 - Handbook Bachelor Degree of Science Academic Session 20212022

Faculty of Science Handbook, Academic Session 2021/2022

4. Laaribi, A. & Peters, L. (2019). GIS and the 2020
3. Dobson, A. J., & Barnett, A.G. (2018). An Introduction Census: Modernizing Official Statistics, Esri Press.
to Generalized Linear Models (4th ed.). Boca Raton,
FL: Chapman and Hall/CRC. 5. Patel, G.S. (2012). Qualitative Research in Education:
4. Faraway, J. J. (2016). Extending the Linear Model with Concept, Methods, Data Analysis and Report Writing,
R: Generalized Linear, Mixed effects and LAP LAMBERT Academic Publishing.
Nonparametric Regression Models (2nd ed.). Boca
Raton, FL: Chapman and Hall/CRC. SIT3003 COMPUTER INTENSIVE METHODS IN
5. Fox, J. (2015). Applied Regression Analysis and STATISTICS
Generalized Linear Models (3rd ed.). Thousand Oaks,
CA: SAGE Publications.
6. Draper, N. R. and Smith, H. (1998). Applied Computer generation of uniform and non-uniform random
variables. Monte Carlo evaluation of integrals. Variance
Regression Analysis (3rd ed.). John Wiley & Sons, Inc.
reduction techniques. Bootstrap and jackknife methods;
Applications in confidence interval construction. Maximum
likelihood estimation of model parameters via the
SIT2010 STOCHASTIC PROCESSES
Expectation-Maximization (EM) algorithm. The Markov Chain
Monte Carlo method.
Definition and examples of stochastic processes: Gambler’s
ruin problem, Brownian motion and Poisson process.
Introduction to simple random walk. Discrete time Markov Assessment:
Chains. Transition probability. Properties of class. Continuous Assessment: 40%
Final Examination:
60%
Transience and recurrence properties. Absorbing
probability. Stationary distribution and limiting probability. References:
Markov chain simulations and applications.
1. Dagpunar, J. S. (2007). Simulation and Monte Carlo.
Chichester: John Wiley.
Assessment: 2. Gentle, J. E., Härdle, W. K. & Mori, Y. (2012).
Continuous Assessment: 40% Handbook of Computational Statistics: Concepts and
Final Examination: 60%
Methods (2nd ed.). Berlin: Springer-Verlag.
3. Rubinstein, R. Y. & Kroese, D. P. (2016). Simulation
References:
1. Durrett, R. (2016). Essentials of Stochastic Processes, and the Monte Carlo method (Vol. 10). John Wiley &
Sons.
3rd ed. Springer 4. Roberts, C.P. & Casella, G. (2005). Monte Carlo
2. Lefebvre, M. (2007) Applied Stochastic Processes.
Springer. Statistical Methods. New York: Springer.
3. Ross, S. M. (1996). Stochastic Processes. Wiley. 5. Ross, S. M. (2012). Simulation (3rd ed.). San Diego,
4. Ross, S. M. (2007) Introduction to Probability Models, CA: Academic Press.
9th edition. Academic Press.
5. Jones, P. W., & Smith, P. (2001). Stochastic SIT3004 APPLIED STOCHASTIC PROCESSES
Processes: An Introduction. Arnold Texts in Statistics.
Time reversible Markov chains. Poisson processes.
Continuous-time Markov chains and birth and death
SIT2011 STATISTICS AND COMMUNITY
processes. Brownian motion. Application to real-world
phenomena, such as in finance.
This course exposes students to some aspects of statistics
in community. The main aim is to highlight the role of official
statistics in society. The topics chosen for this course come Assessment: 40%
Continuous Assessment:
from a variety of different areas, for example, statisticians Final Examination: 60%
and their work, statistics and technology, and statistics and
society. Students will work in groups on projects related to
the topics discussed in lectures. Students will use elements References:
of statistics in the planning a community project including 1. Durrett, R. (2016). Essentials of stochastic processes,
Second Edition, Springer.
designing questionnaire, collecting/managing/analyzing 2. Ross, S. M. (2003). An introduction to probability
data and reporting the findings. Each group is required to
identify and plan activities for a community partnership that models, Eighth Edition, Academic press.
will not only help them to enhance their understanding or 3. Kao, E. P. C. (1997). An introduction to stochastic
gain a different perspective of their project but will also be 4. processes. Duxbury Press.
Ross, S. M. (1996). Stochastic processes, Second
beneficial to the community partner. Each student will be Edition, John Wiley.
required to record a reflection of their experiences before,
during and after the field work at the community partner and
to submit their record with the group project report at the end
of the semester. Students are also required to do a group SIT3005 TIME SERIES AND FORECASTING
METHODS
presentation based on the project.
Introduction to time series and forecasting. Time series
Assessment:
Continuous Assessment: 100% graphics. Simple forecasting methods. Transformation and
adjustments. Fitted values, residuals and prediction
intervals. Time series regression. Time series
References: decomposition. Exponential smoothing. ARIMA models.
1. Kementerian Pendidikan Tinggi, Jabatan Pendidikan
Tinggi (2019) Chapter 1: Sulam as Community ARCH and GARCH models.
Engaged Pedagogy.
2. Harris, D.F. (2014). The Complete Guide to Writing Assessment: 40%
Continuous Assessment:
Questionnaires: How to Get Better Information for
Better Decisions, I&M Press. Final Examination: 60%
3. Beatty, P.C., Collins, D., & Kaye, L. (2019). Advances
in Questionaire Design, Development, Evaluation and
Testing, Wiley.
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