| CODE | SOR3210 | ||||||||||||
| TITLE | Multivariate Analysis 1 | ||||||||||||
| 今日黑料 LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| EQF/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: - Explain the role of the multivariate normal distribution in estimation and hypothesis testing of mean vectors and variance–covariance matrices; - Describe and critically evaluate key techniques used in multivariate hypothesis testing, with emphasis on likelihood ratio tests; - Demonstrate a solid understanding of multivariate estimation theory and its underlying assumptions; - Explain the role of matrix algebra in multivariate statistical analysis, particularly in modelling and interpreting covariance structures; - Explain the principles and assumptions underlying discriminant analysis, and its use in classifying observations into predefined groups; - Explain the principles underlying classification methods for assigning observations to predefined groups; - Explain the theoretical foundations of methods used in the analysis of longitudinal and multilevel data. 2. Skills By the end of the study-unit the student will be able to: - Apply multivariate statistical techniques to analyse real-world datasets and interpret the results appropriately; - Select appropriate multivariate methods for a given problem and justify their use; - Compare competing statistical models using appropriate criteria and assess model adequacy and goodness of fit; - Use statistical software to perform simulations and conduct multivariate data analysis; - Critically interpret and extend learned concepts to understand and apply advanced multivariate techniques beyond those explicitly covered in the 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 2026/7. It may be subject to change in subsequent years. |
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