| CODE | SOR2510 | ||||||||||||
| TITLE | Statistical Methods for Information and Communication Technology | ||||||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 5 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||
| DESCRIPTION | The study-unit will provide a basic introduction to core statistical concepts and methods thus providing a sound framework for the application of statistical methods to ICT postgraduate students. The study-unit will have the following topics: - Familiarisation with R; - Parametric tests; - Non parametric tests; - Regression; - Generalised Linear Models; - Principal components and factor analysis; - Discriminant analysis; - Techniques in time series analysis; - Stochastic simulation; - Applications in Queuing theory. Study-Unit Aims: The aim of this study-unit is to provide postgraduate students with additional statistical skills to those obtained in their undergraduate courses. These includes standard modelling and hypothesis testing in R (a statistics programming language), and additional techniques which are of specific interest to ICT students. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Identify the correct statistical tests to analyse a data set; - Code statistical tests using software; - Interpret the output obtained from software output and draw the correct conclusion; - Use R programming language to implement the statistical methods discussed; - Describe the basics of an array of statistical techniques and models such as regression, GLMs, PCA, discriminant analysis and time series models for the appropriate datasets; - Discuss the basics of simulation; - Describe queuing theory concepts for practical scenarios. 2. Skills: By the end of the study-unit the student will be able to: - Program statistical concepts in R; - Implement and draw conclusions from statistical tests using software; - Interpret the output obtained from software output and draw the correct conclusion; - Provide a written report which contains a thorough analysis of a data set; - Implement an array of statistical techniques and models such as regression, GLMs, PCA, discriminant analysis and time series models on the appropriate datasets; - Simulate from specific distributions, and also conduct some techniques where simulation is useful (e.g. integration, resampling); - Implement queuing theory concepts (both analytical and simulation based) in practical scenarios. Main Text/s and any supplementary readings: Main Texts: -The R Manuals: https://cran.r-project.org/manuals.html. - Asmussen, S. and Glynn, P. (2007) Stochastic Simulation: Algorithms and Analysis. Springer. - Shumway, R.S. and Stoffer, D.S. (2017). Time Series Analysis and Its Applications (With R Examples). Springer. Supplementary Readings: - Kelton, W.D. (2006). Simulation with Arena. McGraw-Hill. - Spiegelhalter, D. (2019) The Art of Statistics: Learning from Data, Pelican. |
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| STUDY-UNIT TYPE | Lecture | ||||||||||||
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| LECTURER/S | Mark A. Caruana Fiona Sammut Monique Sciortino David Paul Suda |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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