| CODE | SOR3210 | ||||||||||||||||
| TITLE | Multivariate Analysis 1 | ||||||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||||||
| MQF LEVEL | 6 | ||||||||||||||||
| ECTS CREDITS | 5 | ||||||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||||||
| DESCRIPTION | - Estimation - Hypothesis Testing - Principal Components - Factor Analysis - Classification Methods - Cluster Analysis - Analyzing Longitudinal Data For the project and presentation, students taking this study unit will also be required to focus on another multivariate technique, such as regularization methods in regression or classification, canonical correlation analysis and multidimensional scaling, which are not included in the list above. Study-unit Aims: The main aim of this study-unit is that of familiarizing the students with the theoretical and practical framework underlying the analysis of any data set that involves more than one variable. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will: - Be able to understand the role that the multivariate normal distribution plays in estimation and hypothesis testing of mean vectors and variance covariance matrices in a multivariate setting; - Be familiar with various techniques that may be used in multivariate hypothesis testing, with special consideration being given to the likelihood ratio test; - Have developed a sound background in multivariate estimation theory; - Have a better appreciation of the role which matrix algebra plays, particularly in analysing covariance structures; - Use prior knowledge on the data to categorize unlabelled information; - Be able to identify groups within a dataset when no prior information about a grouping structure is available; - Have the theoretical foundations required to be able to analyse longitudinal data sets. 2. Skills By the end of the study-unit the student will be able to: - Use the theoretical knowledge gained in the study unit to analyse real multivariate data; - Know which techniques are suitable in specific contexts; - Know how to compare competing models and also check the goodness of fit of a model; - Use various statistical packages to perform simulations and to analyse real multivariate data; - Use the material learnt to understand the theoretical and practical importance of multivariate techniques which have not been covered in this study-unit. Main Text/s and any supplementary readings: Main Texts: - Mardia, K.V., Kent, J.T. and Bibby, J.M. (1995) Multivariate Analysis, Academic. - Hair J., Anderson R., Tatham R. and Black W., (1998) Multivariate Data Analysis, Prentice Hall I. - Rencher A.C. and Christensen W.F. (2012) Methods of Multvariate Analysis, 3rd Edition. Wiley Series in Probability and Statistics. - Song, P.X. -K. (2007) Correlated Data Analysis: Modeling, Analytics and Applications, Springer. Supplementary Texts: - Knight, K. (1999) Mathematical Statistics, Chapman & Hall. - Srivastava, M.S. and Khatri, C.G. (1979) An Introduction to Multivariate Statistics, North Holland. - Johnson, R.A. and Wichern, D.W. (1992) Applied Multivariate Statistical Analysis, Prentice Hall Inc. - Flury, B. (1997) A First Course in Multivariate Statistics, Springer. - Verbeke, G. and Molenberghs, G. (2009) Linear Mixed Models for Longitudinal Data, Springer. |
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| ADDITIONAL NOTES | Pre-requisite Study-units: SOR1110, SOR2211 & SOR2221 | ||||||||||||||||
| STUDY-UNIT TYPE | Lecture and Practical | ||||||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Monique Borg Inguanez Fiona Sammut |
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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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