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TIME SERIES ANALYSIS

Stationary and non-stationary series: removal of trend and seasonality by differencing. Moments and auto-correlation. Models: simple AR and MA models (mainly AR(1), MA(1)): moments and auto-correlations; the conditions of stationarity: invertibility. Mixed (ARMA) models, and the AR representation of MA and ARMA models. Yule-Walker equations and partial auto-correlations (showing forms for simple AR, MA models). Examples showing simulated series from such processes, and sample auto-correlations and partial auto-correlations.  (Other models, e.g., trend and seasonal). Model identification: Elementary ideas of identification of models based on simple acf and pacf showing difficulties with real series. Estimation of parameter: initial estimate based on sample acf and pacf only (least squares estimates by iterative method). Result for standard error of sample acf, pacf and estimators. Forecasting: use of the AR representation for forecasting. Minimum mean square error forecasts. Updating.

 

Course Code: 
STA 815
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
First Semester
Pre-requisite: 
STA 406
Select Programme(s): 
Statistics

ADVANCED TOPICS IN OPERATIONS RESEARCH

Formulating Linear Programming Models:  Goal programming, Transportation problem, Case study.  Mathematical Programming: Project planning and control, Dynamic programming,

Integer programming.  Probabilistic Models: Application of queuing theory, Forecasting and simulation, Decision analysis (making hard decisions), Multi-criteria decision making.

 

Course Code: 
STA 807
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
First Semester
Pre-requisite: 
MAT409
Select Programme(s): 
Statistics

MULTIVARIATE METHODS

Multivariate data summary and graphical displays. Multivariate normal distributions: Estimation of mean and covariance, one- and two-sample problems, analysis of variance.

Reduction of dimensionality: principal components and factor analyses. Discrimination and classification. Correlation; partial, multiple and canonical. Non-metric problems: clustering and scaling.

 

 

Course Code: 
STA 806
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
Second Semester
Pre-requisite: 
STA 405
Select Programme(s): 
Statistics

STATISTICAL INFERENCE AND BAYESIAN METHODS

Criteria of choice, and optimality consideration, in respect of point estimation, hypothesis tests and confidence intervals.  Likelihood methods with special consideration of maximum likelihood estimates (m.l.e.) and likelihood ratio tests including multiparameter problems (and linearisation methods).  Specific techniques will include:  Hypothesis Testing:

Pure significance tests, simulation tests, Neyman Pearson Lemma, UMP test.  Point Estimations: Efficiency, consistency, minimum variance bound estimators.  Determination of m.l.e’s including linearisation and asymptotic properties, maximum likelihood ratio tests and large-sample equivalents, asymptotic           optimality. Score tests.  Jackknifing, bootstrapping. Prior distributions: Representation of prior information via a prior distribution, substantial information, vague priors and ignorance, empirical Bayes ideas. Normal Models: Theory for  unknown), prior-posterior-predictive, normal regression model. Comparisons: Comparisons of classical, Bayesian, decision-theory approaches and conclusions via specific examples.

 

 

Course Code: 
STA 805
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
First Semester
Pre-requisite: 
STA 402
Select Programme(s): 
Statistics

SAMPLING TECHNIQUES AND SURVEY METHODS

The necessity and practical use of sample surveys: sample versus census, presentation and organisation of a survey. Methods of sampling. Simple random samples: techniques, estimation, choice of sample size. Ratio and regression estimators. Stratified random sampling: criteria for good stratification before or after sampling. Quota sampling.  One-stage and two-stage cluster sampling. Systematic sampling. Comparison and choice of estimators. Estimation of treatment contrasts and their precision. 

Course Code: 
STA 804
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
Second Semester
Pre-requisite: 
STA 302
Select Programme(s): 
Statistics

ADVANCED STATISTICAL METHODS

Exploratory Data Analysis: Data display, histograms, stem-and-leaf plots, box plots, data summary and   description. Elementary Methods: Single-and two-sample problems, standard normal-theory tests and estimators,       departures from assumptions, Poisson, Binomial and multinomial models, dispersion tests, goodness-of-fit, two-way contingency table. Regression Methods: Linear, multi-linear and polynomial regression, estimation of parameters, examination of residual, model checking.  Analysis of variance: One- and two-way analyses of variance. Examination of residuals. Unbalanced case.

Course Code: 
STA 803
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
First Semester
Pre-requisite: 
STA 403
Select Programme(s): 
Statistics

PROBABILITY AND STOCHASTIC PROCESSES

Random point processes in time and space: Poisson process, inhomogeneous, compound and spatial generalizations. Review of transient and stability of random phenomena: the use of discrete time renewal theory, including the renewal theorem (without proof) with examples. Population models: discrete branching process, birth-and-death process, simple queuing models. Discrete time Markov chain: transition probabilities, classification of stages, equilibrium and absorption probabilities.

Course Code: 
STA 802
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
Second Semester
Pre-requisite: 
STA 404
Select Programme(s): 
Statistics

DATA ANALYSIS

In this course the student will be cast in the role of a practicing statistician and become involved in projects within many fields of applications. Each of the selected projects has to be written in a report, some of which will be presented orally at seminars.  Some presentations may be done jointly with other students.  These are important aspects in the training of a practicing statistician, who must be able to present findings in a concise, but lucid manner, which can be, understood even by non-statisticians.  The reports are continuously assessed, each project being graded and returned quickly. Some of the projects are designed to illustrate basic statistical techniques from various courses and methods and to introduce the use of the statistical packages MINITAB, GLIM, PLUM, GENSTAT AND SPSS.  Others may be more open-ended or require more specialist techniques. The course opens with one or two non-assessed exercises designed to make the student familiar with the computing facilities. 

Course Code: 
STA 816
No. of Credits: 
3
Level: 
Level 800
Course Semester: 
Second Semester
Pre-requisite: 
STA 304 & STA 401
Select Programme(s): 
Statistics
16 Jul, 2020

        Vacancy: Postdoctoral Fellows – 3 Positions

The Centre for Coastal Management (CCM) at the University of Cape Coast (UCC) invites applications for a 2-year Postdoctoral Fellowship on the Africa Centre of Excellence in Coastal Resilience (ACECoR) Project in Ghana, from eligible and interested candidates.

About the Fellowship 

The ACECoR Postdoctoral Fellowships Program has been instituted to attract outstanding recent doctoral graduates to the University of Cape Coast from across the West and Central Africa sub-regions. The Program aims to recruit young researchers who have the potential to build and lead collaborative research activities across the thematic research areas and support research activities of the

Centre. The University of Cape Coast offers a conducive research environment with a well-resourced laboratory facility for coastal areas research.

Job/Research Area

It includes areas of research priority for the CCM’s ACECoR Project; CoastalGeomorphology and Engineering, Blue Economy, Climate Change Adaptation and Mitigation, Disaster Risk Management and Migration, and Ecosystems, and Biodiversity.

Location 

University of Cape Coast, Cape Coast, Ghana.

Eligibility/Qualification

 Suitable applicants must have completed their PhD studies within the past three years (from 2017). The postdoctoral fellow must have a background in one or a combination of the

following areas; Fisheries and/or Aquaculture, Integrated Coastal Zone Management (ICZM), Oceanography, Climate Science, Economics, Environmental Sciences, Sociology, and other fields related to

the research priority areas of ACECoR. Competencies in the use of spreadsheet, statistical analysis (quantitative and qualitative) and good knowledge and application of Geographic Information System (GIS)

and Remote Sensing (RS) are required and will give suitable candidates a competitive advantage.

Job/Position Description  

The Fellows will work within the scope of the priority/thematic areas of research at ACECoR and will be assigned the following responsibilities:

1. Conduct research required in the assigned thematic area to the highest standard and in accordance with all institutional and national regulations,

2. Liaise with research theme leads and staff of CCM to design and implement research instruments, protocols and other tools for laboratory and field data collection and analyses,

3. Coordinate the acquisition of ethical clearance for all research at the Centre and support the coordination of students’ research activities,

4. Contribute to the development of grant-winning research proposals for the Centre,

5. Support the datahub outfit of ACECoR in collating and managing data from students, and

6. Perform any other assignment as shall be assigned by the Director of CCM/ACECoR

Salary/Remuneration 

Benefits attached to this position are attractive.

How to apply

 All suitable applicants should submit their application to the Director, Centre for Coastal Management, University of Cape Coast on or before Friday, August 14, 2020. All applications should include

1) Application letter

2) Curriculum Vitae

 3) Letter of motivation

4) Two reference letters

 5) Five-page research proposal.

Send your application via email acecor@ucc.edu.gh under the subject ACECoR Postdoc Fellowship.    

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