SDST3621 Statistical data analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 50
Course Co-ordinator Prof E C H Fong, SCDS (Department of Statistics and Actuarial Science) < chefong@hku.hk >
Teachers Involved (Prof E C H Fong,Statistics & Actuarial Science)
Course Objectives Building on prior coursework in statistical methods and modeling, students will gain a deeper understanding of the entire process of data analysis, using both frequentist and Bayesian tools. The course aims to develop skills of model selection and hypotheses formulation so that questions of interest can be properly formulated and answered. An important element deals with model review and improvement, when one's first attempt does not adequately fit the data. Students will learn how to explore the data, build reliable models, and communicate the results of data analysis to a variety of audiences.
Course Contents & Topics Descriptive statistics, presentation and visualization of data; Simple statistical analyses for the one-sample and two-sample case; Regression analyses: model fitting; variable selection and model diagnostic checking; Bayesian inference: prior distributions, conjugate models, posterior summaries, Bayesian hypothesis testing; Analysis of Variance (ANOVA).

Real data sets will be presented for modelling and analysis using statistical software for gaining hands-on experience.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 identify the association among several continuous or discrete variables
CLO 2 summarize and describe quantitative and qualitative properties of data using simple appropriate statistical measures
CLO 3 carry out comprehensive statistical analyses on real data using frequentist and conjugate Bayesian techniques for regression, hypothesis testing and model selection
CLO 4 formulate testable hypotheses, carry out appropriate statistical inferences, interpret findings and summarize in written reports
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3907
(Students are strongly recommended to take SDST2604 prior to taking 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 Major in Decision Analytics ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2024 Major in Statistics < PLO 1,2,3,4,5,6 >
2023 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2023 Major in Statistics < PLO 1,2,3,4,5,6 >
2022 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2022 Major in Statistics < PLO 1,2,3,4,5,6 >
2021 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
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 and a class test) 50.0 1,2,3,4
Examination One 3-hour written examination 50.0 1,2,3,4
Required/recommended reading
and online materials		 1. An Introduction to Statistical Learning, with Applications in R, by James, Witten, Hastie and Tibshirani (Springer, 2013)

2. Learning R: A Step-by-Step Function Guide to Data Analysis, by Richard Cotton (Author)

3. Bayesian Data Analysis, by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin (CRC Press, 2013). http://www.stat.columbia.edu/~gelman/book/
Course Website http://moodle.hku.hk
Additional Course Information