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SAYT1016 Unlocking the Secrets of Randomness: from Paradoxes to Sport Predictive Modeling to Algorithmic Trading 解開未知的秘密:由悖論到運動預測模型到算法交易
Course Outline:
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(Last update on: 4 March 2021)



Key facts for Summer 2021:

Date:   26 – 30 July, 2* August 2021 (30 hours)
Time:   9:30am – 12:30pm, 2:30pm – 5:30pm
Teaching Platform:   Face to Face  (The Chinese University of Hong Kong) #
Enrollment:   50
Expected applicants:   Students who are promoting to S4-S5 with good knowledge in mathematics
and with strong interest in solving real problems
Tuition Fee:   HKD 3,900.00
(Students who have attended all sessions will be granted a HKD 1,000 scholarship)
Lecturer:   Prof. CHAN Kin Wai
* This date is reserved for make-up classes in case there is any cancellation of classes due to unexpected circumstances.
# This course is offered face-to-face lessons at CUHK campus. It may switch to online teaching in accordance with the pandemic development and the policy of the university.


Uncertainty exists in many real-life problems, ranging from stock returns to sport results to medication effects to election outcomes. Statistics offers methods to handle uncertainty with a higher precision. Improving a decision with 50-50 certainty to 60-40 certainty makes a huge difference in many practically problems. This course introduces ways to (i) define, (ii) model and (iii) forecast uncertainty through real-life examples and counterintuitive phenomena. Topics include (i) birthday paradox, Simpson’s paradox; (ii) linear regression model, auto-regressive regression model, logistic regression model, non-parametric regression model; and (iii) historical simulation, and k-mean clustering.

不確定性存在於許多現實生活中的問題,例子涵蓋股票回報、運動結果、藥物效果、選舉結果等。統計科學提供了具更高準定性的方法,以處理不確定性的問題。在許多實際問題中,將50-50的不確定性提高至60-40,可令數據分析變得更精確。本課程以實際示例和違反直覺的現象引導,來 (i) 定義、(ii) 模型和 (iii) 預測不確定性。主題包括 (i)生日悖論,辛普森悖論;(ii) 線性迴歸模型,自迴歸模型,邏輯迥歸模型,非參數迴歸模型;(iii) 歷史模擬法,k平均演算法。


Organising units:
  • Department of Statistics, CUHK
  • Centre for Promoting Science Education, CUHK
Category:   Category I – University Credit-Bearing
Learning outcomes:   Upon completion of this course, students should be able to:
  1. properly define and write down probabilistic statements, appropriately use conditional statements, and correctly use expected values;
  2. sensibly build simple statistical models for continuous, binary, dependent variables, and construct simple nonparametric models; and
  3. produce reasonable predictions for supervised and unsupervised problems by various methods.
Learning Activities:
  1. Lectures
  2. Lab
  3. Case Discussion
  4. Projects
Medium of Instruction:   Cantonese supplemented with English
  1. Short answer test or exam
  2. Presentation
Recognition:   No. of University unit(s) awarded: 1
* Certificate or letter of completion will be awarded to students who attain at least 75% attendance and pass the assessment (if applicable)
Expected applicants:   Students who are promoting to S4-S5 with good knowledge in mathematics and with strong interest in solving real problems
Organising period:   Summer 2021
Application method:   SAYT Online application