SDST3600 Linear statistical analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Dr C W Kwan, SCDS (Department of Statistics and Actuarial Science) < cwkwan@hku.hk >
Teachers Involved (Dr C W Kwan,Statistics & Actuarial Science)
(Mr H Y Y Cheung,Statistics & Actuarial Science)
Course Objectives The analysis of variability is mainly concerned with locating the sources of the variability.  Many statistical techniques investigate these sources through the use of 'linear' models.  This course presents the theory and practice of these models.
Course Contents & Topics (1) Simple linear regression: least squares method, analysis of variance, coefficient of determination, hypothesis tests and confidence intervals for regression parameters, prediction.
(2) Multiple linear regression: least squares method, analysis of variance, coefficient of determination, reduced vs full models, hypothesis tests and confidence intervals for regression parameters, prediction, polynomial regression.
(3) One-way classification models: one-way ANOVA, analysis of treatment effects, contrasts.
(4) Two-way classification models: interactions, two-way ANOVA for balanced data structures, analysis of treatment effects, contrasts, randomised complete block design.
(5) Universal approach to linear modelling: dummy variables, 'multiple linear regression' representation of one-way and two-way (unbalanced) models, ANCOVA models, concomitant variables.
(6) Regression diagnostics: leverage, residual plot, normal probability plot, outlier, studentized residual, influential observation, Cook's distance, multicollinearity, model transformation.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand linear regression model with one or multiple independent variables
CLO 2 understand ANOVA models for one and two factors
CLO 3 understand general linear model with categorical and continuous independent variables
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in BIOF2014 or SDST2602; and
Not for students who have passed in SDST3907, or have already enrolled in this course.
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 ( Core/Compulsory )
2024 Major in Risk Management ( Core/Compulsory )
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 ( Core/Compulsory )
2023 Major in Risk Management ( Core/Compulsory )
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 ( Core/Compulsory )
2022 Major in Risk Management ( Core/Compulsory )
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 ( Core/Compulsory )
2021 Major in Risk Management ( Core/Compulsory )
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 Risk Management < PLO 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 Risk Management < PLO 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 Risk Management < PLO 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 Risk Management < PLO 2,3,4,5 >
2021 Major in Statistics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    2nd sem    Examination Dec    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 test) 40.0 1,2,3
Examination One 2-hour written examination 60.0 1,2,3
Required/recommended reading
and online materials		 Michael H Kutner, Christopher J. Nachtsheim, John Neter, William Li: Applied Linear Statistical Models (McGraw-Hill/Irwin; 5th edition)
Berry, D. A. & Lindgren, B. W.: Statistics: Theory and Methods (Duxbury Belmont, 1996)
Draper, N. R. & Smith, H.: Applied Regression Analysis (Wiley, New York, 1998)
Krzanowski, W. J.: An Introduction to Statistical Modelling (Arnold, London, 1998)
Montgomery, D. C. & Peck, E. A.: Introduction to Linear Regression Analysis (Wiley, New York, 1992)
Course Website http://moodle.hku.hk
Additional Course Information NIL