Page 54 - PROSPECTUS FEA IT 17-18

◄Faculty of Economics and Administration►

development of a research project to preparation of the
report. The stages include identification of a research
question, designing a study, literature review (analysis,
synthesis and criticism of current research and theory), data
collection, data analysis, analysis of the findings to answer
the research questions, and drawing appropriate
conclusions.
Assessment Methods 100% Research Project

EQC 7003 STATISTICAL METHODS

Learning Outcomes At the end of the course, students are able to:

(1) Identify different probability distribution and inferential
statistics;
(2) Relate sampling distributions to estimation; and
(3) Evaluate results of appropriate statistical techniques
(parametric or non-parametric) in solving problems, in
business, economics, finance and social science.

Synopsis of Course The course begins with common distributions of random
Contents variables, and is followed by leading into the introduction of
sampling distributions, the conceptual and quantitative tools
in the topics of estimation and hypothesis testing as well as
non-parametric methods. It deals with the fundamentals of
statistics with emphasis on real-life applications in business,
economics, finance, management, and social science.
Assessment Methods Continuous Assessment: 50%
Final Examination: 50%
Main Reference (1) D.D. Wackerly, W. Mendenhall, R.L. Scheaffer.
`Mathematical statistics with Applications', 7th ed.
Duxbury, 2008.
(2) J.L. Devore, K.N. Berk, ‘Mathematical Statistics with
Applications', 2nd ed. Springer, 2012.
(3) W.L. Carlson and B Thorne. ‘Applied Statistical Methods
for Business, Economics and the Social Sciences,
Prentice Hall, 1997.

EQC 7004 STATISTICAL DATA ANALYSIS
Learning Outcomes At the end of the course, students are able to:

(1) Analyse quantitatively the structure in a set of data;
(2) Apply the appropriate statistical techniques given the
aim of analysis in solving the related problems; and
(3) Explain the results arising from the application of these
techniques to data in various fields.
Synopsis of Course This course exposes students to the analysis of univariate
Contents and multivariate data. Students learn to examine variation in
data; assess the need for transformation; evaluate patterns;
summarize the information; and apply various statistical
techniques of analysis. Statistical software is used to teach
the application of regression analysis, discriminant analysis,
principal components analysis, factor analysis and cluster
analysis to data from various fields.
Assessment Methods Continuous Assessment : 50%
Final Examination : 50%

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