Page 136 - Handbook Bachelor Degree of Science Academic Session 20202021

Faculty of Science Handbook, Academic Session 2020/2021

Soft Skills: Assessment:
CS3, CTPS3 Continuous Assessment: 40%
Final Examination: 60%
References:
1. Ross, S. M. (2003). An introduction to probability Medium of Instruction:
models (8 ed.). Academic press. English
th
2. Kao, E. P. C. (1997.) An introduction to stochastic
processes. Duxbury Press. Soft Skills:
nd
3. Ross, S. M. (1996). Stochastic processes (2 ed.). CS2, CTPS2
John Wiley.
4. Durrett, R. (2012). Essentials of stochastic processes References:
nd
(2 ed.). Springer. 1. S. Weisberg (2005). Applied linear regression (3 ed.).
rd
Wiley.
rd
2. A. Agresti (2013). Categorical data analysis (3 ed.).
SIT3005 TIME SERIES AND FORECASTING METHODS Wiley.
3. P. McCullagh, & J. A. Nelder. (1989). Generalized linear
Introduction to time series: data, properties, examples. models (2 ed.). Chapman& Hall.
nd
4. R. H. Myers. (1990). Classical and modern regression
nd
Introduction to forecasting: Forecasting methods, errors in with applications (2 ed.). Duxbury/Thompson.
forecasting, choosing a forecasting techniques, qualitative 5. R. R. Hocking. (2013). Method and applications of linear
and quantitative forecasting techniques. models: Regression and the analysis of variance (3
rd
ed.). Wiley.
Time series regression: Modelling trend, detecting
autocorrelation, type of seasonal variation, modelling SIT3007 DATA ANALYSIS II
seasonal variation, growth curve models, handling first-order
autocorrelation. Introduction to different kind of data; Generalizing the linear
regression models including nonlinear regression model,
Averaging methods: Moving average, simple exponential linear regression in time series data, logistic regression and
smoothing, tracking signals, Holt’s method, Holt-Winters Poisson regression models for categorical response data
method, damped trend exponential method. and selected topics
Box-Jenkins methods: Stationary and non-stationary data,
difference, autocorrelation and partial autocorrelation Practical survey sampling: Selected case study, design of
functions, non-seasonal modeling (ARIMA models), study, questionnaires, collecting data, data analysis, oral
diagnostic checking, forecasting. and written presentation

ARCH and GARCH models. Statistical consulting: Theoretical and practical aspects of
statistical consulting, Communication skill
Assessment: Report writing
Continuous Assessment: 40%
Final Examination: 60% Assessment:
Continuous Assessment: 50%
Medium of Instruction: Final Examination: 50%
English
Medium of Instruction:
Soft Skills: English
CS3, CTPS3
Soft Skills:
References: CS4, CTPS3, TS5
1. Hyndman, R.J., & Athanasopoulus, G. (2018).
Forecasting: principles and practice. Retrieved from References:
https://www.otexts.org/fpp 1. S-Plus 2000 guide to statistics (Vols. 1-2). Mathsoft
2. Makridakis, S., Wheelwright, S.C., & Hyndman, R.J. corporation.
(1998). Forecasting methods and applications. Wiley. 2. Cramer, D. (2003). Advanced quantitative data
3. Montgomery, D. C., Jennings, C. L., & Kulahci, M. analysis. Open University Press.
(2008). Introduction to time series analysis and 3. Evans, J.R., & Olson, D.L. (2007). Statistics, data
forecasting. Wiley. analysis, and decision modeling. Prentice Hall
4. Brockwell, P.J., & Davis, R. A. (2002). Introduction to 4. Miller, D.C., & Salkind, J. (1983). Handbook of research
nd
time series analysis and forecasting (2 ed.). Springer. design and social measurements. Sage Publication.
5. Box, G.E.P., Jenkins, G.W., & Reinsel, G. (2011). Time 5. Derr, J. (2000). Statistical consulting: A guide to
series analysis, forecasting and control (4 ed.). effective communication. Pacific Grove: Duxbury.
th
Prentice Hall. 6. Jarman, Kristin H. (2013). Art of data analysis: How to
Answer almost any question using basic statistics. John
Wiley & Sons
SIT3006 FURTHER TOPICS IN REGRESSION
ANALYSIS
SIT3008 INTRODUCTION TO SURVEY SAMPLING
Multiple Linear Regression Model: Simultaneous Inference,
criteria for selecting model, influence diagnostics and multi- Techniques of statistical sampling with applications in the
collinearity. Introduction to logistic regression and Poisson analysis of sample survey data. Topics include simple
regression: maximum likelihood estimates of the random sampling, stratified sampling, systematic sampling,
parameters, lack of fit test, tests based on deviance and cluster sampling, two-stage sampling and ratio and
score. regression estimates.

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