SDST4602 Multivariate data analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 50
Course Co-ordinator Prof Y Cao, SCDS (Department of Statistics and Actuarial Science) < yuancao@hku.hk >
Teachers Involved (Prof Y Cao,Statistics & Actuarial Science)
Course Objectives In many designed experiments or observational studies, the researchers are dealing with multivariate data, where each observation is a set of measurements taken on the same individual.  These measurements are often correlated.  The correlation prevents the use of univariate statistics to draw inferences.  This course develops the statistical methods for analysing multivariate data through examples in various fields of application and hands-on experience with the statistical software SAS.
Course Contents & Topics Problems with multivariate data.  Multivariate normality and transforms.  Mean structure for one sample.  Tests of covariance matrix.  Correlations: Simple, partial, multiple and canonical.  Multivariate regression.  Principal components analysis.  Factor analysis.  Problems for means of several samples.  Multivariate analysis of variance.  Discriminant analysis.  Classification.  Multivariate linear model.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 analyze multivariate data with main SAS procedures, such as PROC IML, PROC REG, PROC CORR, PROC CANCORR, PROC PRINCOMP, PROC FACTOR, PROC DISCRIM, PROC CANDISC and etc
CLO 2 compare the mean structure of multiple measurements for one or more than one population(s) by multivariate MANOVA and profile analysis
CLO 3 investigate the linear associations among one/two group(s) of variables by multiple, partial and canonical correlation and multivariate regression
CLO 4 explore the latent linear structure of a data set with multiple measurements by principal components analysis and factor analysis
CLO 5 classify observations of a population with one or more than one measurements by discriminant analysis
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3907
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2024 Major in Decision Analytics ( Disciplinary Elective )
2024 Major in Statistics ( Core/Compulsory )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Statistics ( Core/Compulsory )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Statistics ( Core/Compulsory )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Statistics ( Core/Compulsory )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2024 Major in Decision Analytics < PLO 1,2,3,4,5 >
2024 Major in Statistics < PLO 1,2,3,4,5,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2023 Major in Decision Analytics < PLO 1,2,3,4,5 >
2023 Major in Statistics < PLO 1,2,3,4,5,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2022 Major in Decision Analytics < PLO 1,2,3,4,5 >
2022 Major in Statistics < PLO 1,2,3,4,5,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2021 Major in Decision Analytics < PLO 1,2,3,4,5 >
2021 Major in Statistics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 50.0 1,2,3,4,5
Examination One 3-hour written examination 50.0 1,2,3,4,5
Required/recommended reading
and online materials		 Johnson, R. A. & Wichern, D. W.: Applied Multivariate Statistical Analysis (Prentice-Hall, 2007, 6th edition)
Mardia K. V., Kent J. T., and Bibby J. M.: Multivariate Analysis (Academic Press, 1979)
Seber G. A. F.: Multivariate Observations (John Wiley & Sons, 1984)
Morrison D. F.: Multivariate Statistical Methods (McGraw-Hill, 1990, 3rd ed.)
Hair J. F., Anderson R. E., Tatham R. L., & Black W. C.: Multivariate Data Analysis (Prentice-Hall, 2006, 6th edition)
Srivastava M. S.: Methods of Multivariate Statistics (John Wiley and Sons, 2002)
SAS Manuals on-line: Use the HELP button.
Course Website http://moodle.hku.hk
Additional Course Information NIL